Our latest publications - SCML

• Sengupta, Poushali; Khadka, Rabindra; Maharjan, Sabita; Eliassen, Frank; Pandey, Shashi Raj & Zhang, Yan [Show all 8 contributors for this article] (2026). Explainability of Complex AI Models with Correlation Impact Ratio. IEEE Transactions on Artificial Intelligence (TAI). p. 14–14. doi: 10.1109/tai.2026.3732283. Full text in Research Archive
• Bhandari, Shailendra; Szorkovszky, Alex; Yazidi, Anis & Lind, Pedro (2026). Evolutionary foraging in grids: Intermittent search dynamics emerge in finite, depletable landscapes. arXiv. doi: 10.48550/ARXIV.2609.39239. Full text in Research Archive Show summary

  How search strategies evolve in finite, depletable landscapes remains a central question in foraging theory. We study this problem with an evolutionary simulation in which agents forage on a two-dimensional toroidal lattice containing non-renewable resources distributed either uniformly or as L\'evy dust. Each agent carries a heritable genome encoding step lengths, velocities, and turning angles, and selection acts on a fitness function combining energetic gain, movement cost, and coverage efficiency. By allowing movement traits to evolve without imposing a prescribed power-law step-length distribution, we test whether the evolved trajectories are better described by intermittent-search or L\'evy-walk dynamics. Strikingly, our results indicate that, in the finite depletion-driven landscapes considered here, evolved search is more consistent with intermittent dynamics than with strict scale-free L\'evy motion. To characterize the effective dynamics of the evolved trajectories, we take the average adjusted coefficient of determination when fitting second- and fourth-order displacement moments to intermittent search and L\'evy-walk models. While a L\'evy-like random walk fits with the numerical results from the evolutionary search very well (mean adjusted \(\bar{R}^2>0.9\) in most tested conditions), the intermittent search achieves a closer fit, with mean adjusted coefficients of determination(\(\bar{R}^2>0.99\)) for all tested resource distributions. This preference holds across the tested grid sizes and resource densities. Five independent evolutionary runs per environment on a $503\times503$ grid at nominal resource density $\rho=0.15$ reproduce this preference for the uniform environment and the five L\'evy-dust environments. Evolution rapidly reshapes the movement genome toward short displacements while retaining a sparse tail of longer relocations, consistent with local exploitation punctuated by occasional transfer. The framework provides a controlled setting for studying how search rules emerge under resource limitation and may inform resource-constrained exploration in autonomous systems. Project webpage: \url{https://evo-foraging.github.io/}

• Brill, Markus; Colley, Rachael; George, Anne-Marie; Lisowski, Grzegorz; Papasotiropoulos, Georgios & Schmidt-Kraepelin, Ulrike (2026). Cycles in Liquid Democracy: A Game-Theoretic Justification. In Calvanese, Diego (Eds.), Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-26). International Joint Conferences on Artificial Intelligence. ISSN 9781956792096. p. 3350–3358. doi: 10.24963/ijcai.2026/372. Full text in Research Archive Show summary

  A common criticism of liquid democracy within the relevant academic literature is that delegation cycles can occur, seemingly resulting in unused voting power. Yet, practitioners argue that delegation cycles are not only unproblematic but are even formed intentionally by participants. To bring theory closer to reality, we introduce a model that captures this strategic behavior under uncertainty. We study the existence, structure and quality of Nash equilibria, revealing that delegation cycles naturally emerge. To complement these findings, we perform computational experiments using best-response dynamics.

• Athanasopoulos, Andreas; George, Anne-Marie & Dimitrakakis, Christos (2026). Probably Correct Optimal Stable Matching under Two-Sided Uncertainty. In Perković, Emilija & Malinsky, Daniel (Ed.), Proceedings of the Forty-second Conference on Uncertainty in Artificial Intelligence (UAI). Proceedings of Machine Learning Research. p. 228–254. doi: https:/proceedings.mlr.press/v337/athanasopoulos26a.html. Full text in Research Archive Show summary

  We study a sequential learning problem for stable matchings in two-sided markets where preferences on both sides are initially unknown. We focus on a centralized setting where an algorithm matches agents at each time step and receives noisy rewards that reflect the preferences of the matched agents, following a semi-bandit feedback structure. We adopt a pure exploration perspective, aiming to efficiently identify the optimal stable matching with high probability. Our work extends prior results by handling \emph{two-sided uncertainty} and by exploiting \emph{partial preference} information. A central ingredient is the notion of \textbf{pervasive stable matching}, which enables the identification of optimal stable matchings under partial preferences. We propose elimination-based algorithms whose stopping criteria exploit the structure of the learned partial preferences, and provide a refined sample-complexity analysis. Beyond pure exploration, we extend our approach to regret minimization and establish regret bounds with respect to the \emph{optimal} stable matching that avoid dependence on the minimum reward gap $\Delta_{\min}$.

• Abdallah, Majd; Nakken, Sigve; Georges, Mikaël; Bierkens, Mariska; Galvis, Johanna & Groppi, Alexis [Show all 15 contributors for this article] (2026). TrialMatchAI: an end-to-end AI-powered clinical trial recommendation system to streamline patient-to-trial matching. Nature Communications. 17(1). doi: 10.1038/s41467-026-70509-w. Full text in Research Archive Show summary

  Patient recruitment remains a major bottleneck in clinical trials, calling for scalable and automated solutions. We present TrialMatchAI, an AI-powered recommendation system that automates patient-to-trial matching by processing heterogeneous clinical data, including structured records and unstructured physician notes. Built on fine-tuned, open-source large language models (LLMs) within a retrieval-augmented generation framework, TrialMatchAI ensures transparency and reproducibility and maintains a lightweight deployment footprint suitable for clinical environments. The system normalizes biomedical entities, retrieves relevant trials using a hybrid search strategy combining lexical and semantic similarity, re-ranks results, and performs criterion-level eligibility assessments using medical Chain-of-Thought reasoning. This pipeline delivers explainable outputs with traceable decision rationales. In real-world validation, 92% of oncology patients had at least one relevant trial retrieved within the top 20 recommendations. Evaluation across synthetic and real clinical datasets confirmed state-of-the-art performance, with expert assessment validating over 90% accuracy in criterion-level eligibility classification - particularly excelling in biomarker-driven matches. Designed for modularity and privacy, TrialMatchAI supports Phenopackets-standardized data, enables secure local deployment, and allows seamless replacement of LLM components as more advanced models emerge. By enhancing efficiency, interpretability and offering lightweight, open-source deployment, TrialMatchAI provides a scalable solution for AI-driven clinical trial matching in precision medicine.

• Victoriano, Margarida; Pavlovic, Milena; Sandve, Geir Kjetil Ferkingstad; Oliveira, Hélder P.; Rocha, Artur & Greiff, Victor (2026). From virtual experiments to biomedical insight with synthetic data. Nature Machine Intelligence. 8, p. 866–879. doi: 10.1038/s42256-026-01244-6. Full text in Research Archive
• Kanetsky, Peter A.; Almstrup, Kristian; Cherlin, Svetlana; Cortessis, Victoria K.; Ferlin, Alberto & Gietema, Jourik A. [Show all 29 contributors for this article] (2026). The Testicular Cancer Consortium (TECAC): Filling Knowledge Gaps in the Genetic Etiology of Testicular Germ Cell Tumors. Andrology. ISSN 2047-2919. doi: 10.1111/andr.70168. Full text in Research Archive
• Balaguera, Nathalia Chica; Andersen, Aram Nikolai; Munoz, Sara Orellana; Llorente, Ignacio Garcia; P, Aurélie Nguéa & Nakken, Sigve [Show all 14 contributors for this article] (2026). Time-resolved functional genomics using deep learning reveals global hierarchical control of autophagy. Nature Cell Biology. ISSN 1465-7392. 28(3), p. 465–479. doi: 10.1038/s41556-025-01837-0. Full text in Research Archive Show summary

  Recycling of cellular components through autophagy maintains homeostasis in changing nutrient environments. Although its core mechanisms are extensively studied, understanding of its systems-wide dynamic regulation remains limited, particularly regarding how autophagy is inactivated once nutrients are restored. Here we mapped the genetic network that controls activation and inactivation of autophagy during nitrogen changes by combining time-resolved high-content imaging, deep learning and latent feature analysis. This dataset, termed AutoDRY, categorizes 5,919 mutants based on nutrient response kinetics and their contributions to autophagosome formation and clearance. Integrating these profiles with functional and genetic network data uncovered hierarchical and multilayered control of autophagy and revealed multiple new regulatory pathways. Notably, we identified the retrograde pathway as a pivotal time-varying modulator that tunes the expression of core autophagy genes and plays a central role in autophagy inactivation. Together, this study establishes a systems-level resource to guide future investigations of autophagy.

• Kranjec, Christian; Eilertsen, Ina; Nunes, Luis; Moosavi, Hossein; Berg, Kaja Christine Graue & Eknæs, Mette [Show all 12 contributors for this article] (2026). Common gene mutations in 103 authenticated colorectal cancer cell lines. Oncogenesis. 15. doi: 10.1038/s41389-026-00599-0. Full text in Research Archive Show summary

  Colorectal cancer (CRC) cell lines represent the main molecular subtypes of tumors and are valuable models for preclinical investigations. However, cell lines can diverge over time and careful selection of models based on their molecular features is key. We have authenticated 103 commonly used CRC cell lines and present the mutation profiles of 20 CRC-relevant genes sequenced to an average depth of 575 times coverage. The cell lines reflected the distinct mutation patterns of hypermutation phenotypes associated with microsatellite instability and pathogenic POLE mutations. Hypermutated cell lines appeared to have a stronger mutational divergence and more frequent subclonal mutations, while mutations not associated with hypermutation were more frequently homozygous or hemizygous, classified as pathogenic, and subject to stronger selection pressure. Loss of heterozygosity at mutated loci was primarily observed in tumor suppressor genes. Genetic interactions based on co-occurring mutations identified cell lines representative of particularly aggressive subtypes of CRC, including concurrent BRAF p.V600 and truncating APC mutations, as well as APC/TP53/RAS triple mutations with double hits of APC. This study provides a resource to guide the selection of cell lines for functional studies of CRC, and detailed mutation data including classifications of pathogenicity, variant allele frequencies and illustrations of the mutation distribution along the length of encoded proteins are included.

• Alsøe, Lene; Brackmann, Stephan Andreas; Liu, Yan; Wennerström, Anna Berit; Esbensen, Qin Ying & Kalyanasundaram, Sumana [Show all 10 contributors for this article] (2026). The Role of Nuclear APOBEC Enzymes in Neoplastic Progression of Ulcerative Colitis. Clinical and Experimental Gastroenterology. 19. doi: 10.2147/CEG.S588161. Full text in Research Archive Show summary

  Aberrant expression of the cytidine DNA deaminase AID in enterocytes within inflamed mucosa in ulcerative colitis (UC) patients has been proposed to be involved in progression of UC to colitis-associated colorectal cancer (CA-CRC). Here, we followed the expression of AID and other nuclear cytidine DNA deaminases of the APOBEC family through several stages of progression of UC in a CA-CRC case (UC progressor) and in a cohort of UC progressors and non-progressors. In inflamed and high-grade dysplastic mucosa, AICDA and APOBEC1 mRNAs, but not APOBEC3-family enzyme mRNAs, were overexpressed compared to non-inflamed tissue. Immunohistochemical staining did not show expression of AID or APOBEC3 enzymes in the enterocytes, neither in specimens from the CA-CRC case nor in biopsies from the progressor and non-progressor cohort. APOBEC1 was highly expressed in the enterocytes throughout the colorectum from the CA-CRC case and in most biopsies examined from the progressor and non-progressor cohort. The expression was not correlated with the histology of the mucosa or the progression status of the patients. In the CA-CRC case, we identified AID- and APOBEC3-associated mutation signatures in inflamed, high-grade dysplasia and cancer genomes. In conclusion, our findings suggest that AID or other nuclear APOBECs are unlikely to drive the progression of UC to CA-CRC late during course of UC. However, we cannot exclude the possibility that bursts of AID and nuclear APOBEC3 activity may contribute to formation of genome instability in early phases of disease development.

• Ekstrøm, Per Olaf; Nakken, Sigve & Hovig, Eivind (2026). Exploring Taq polymerase induced mutations in part of BRAF exon 15 by sequencing and mutation enrichment. Mutation Research - Fundamental and Molecular Mechanisms of Mutagenesis. ISSN 0027-5107. 832. doi: 10.1016/j.mrfmmm.2026.111934. Full text in Research Archive
• Zobolas, John; George, Anne-Marie; Alberto, López,; Sebastian, Fischer,; Marc, Becker, & Aittokallio, Tero Antero (2026). Prognostic biomarker discovery in pancreatic cancer through hybrid ensemble feature selection and multi-omics data | BioData Mining | Springer Nature Link. BioData Mining. doi: 10.1186/s13040-026-00546-0. Full text in Research Archive Show summary

  Accurate prediction of patient survival using high-dimensional multi-omics data requires effective feature selection methods that balance predictive performance, sparsity, and stability to enable reproducible and transparent prognostic biomarker discovery. Existing approaches often rely on ad hoc thresholds, limiting their reproducibility and clinical utility. We developed a hybrid ensemble feature selection (hEFS) framework that integrates data subsampling with multiple prognostic models, combining embedded and wrapper-based strategies for survival prediction. Features are ranked using a voting-theory-inspired aggregation approach across models and subsamples, while the optimal feature subset is determined via Pareto front optimization, eliminating the need for user-defined thresholds. We benchmarked hEFS against conventional CoxLasso using 100 Monte Carlo cross-validation iterations on multi-omics datasets from three pancreatic ductal adenocarcinoma (PDAC) cohorts, assessing sparsity, stability, redundancy, discrimination, and computational cost. Across all cohorts, hEFS selected substantially fewer and more stable features than CoxLasso (~10 vs. ~60 per modality), with lower variance, while still maintaining comparable discrimination performance with clinical baselines (C-index ~0.54–0.60). Feature redundancy was low, and ensemble subsampling further improved stability. Prognostic performance depended mainly on modality choice, rather than integration model, with gene expression plus clinical variables achieving the highest discrimination power (C-index ~0.64). hEFS provides a fully automated, interpretable, and reproducible framework for multi-omics biomarker discovery in high-dimensional survival analysis. By producing sparse, yet stable biomarker panels without loss of predictive power, hEFS represents a practical tool for integrative multi-omics modeling and it is openly available in the mlr3fselect R package.

• Taylor, Kristofer G.; Johannessen, Bjarne; Mills, Ian Geoffrey; Rye, Morten Beck; Axcrona, Karol & Skotheim, Rolf (2026). Spatial Robustness of Prostate Cancer Biomarkers Evaluated by Spatial Transcriptomics. The Prostate. ISSN 0270-4137. doi: 10.1002/pros.70164. Full text in Research Archive
• Ferenc, Katalin Terezia; Martini, Lorenzo; Rauluseviciute, Ieva; Sandve, Geir Kjetil Ferkingstad & Mathelier, Anthony (2026). inMOTIFin: a lightweight end-to-end simulation software for regulatory sequences. Bioinformatics. ISSN 1367-4803. doi: 10.1093/bioinformatics/btag026. Full text in Research Archive Show summary

  The accurate development, assessment, interpretation, and benchmarking of bioinformatics frameworks for analyzing transcriptional regulatory grammars rely on controlled simulations to validate the underlying methods. However, existing simulators often lack end-to-end flexibility or ease of integration, which limits their practical use. We present inMOTIFin, a lightweight, modular, and user-friendly Python-based software that addresses these gaps by providing versatile and efficient simulation and modification of DNA regulatory sequences. inMOTIFin enables users to simulate or modify regulatory sequences efficiently for the customizable generation of motifs and insertion of motif instances with precise control over their positions, co-occurrences, and spacing, as well as direct modification of real sequences, facilitating a comprehensive evaluation of motif-based methods and interpretation tools. We demonstrate inMOTIFin applications for the assessment of de novo motif discovery, the analysis of transcription factor cooperativity, and the support of explainability analyses for deep learning models. inMOTIFin ensures robust and reproducible analyses for studying transcriptional regulatory grammars.

• Lapao, Ana Sofia Da Silva; Johnson, Lauren Sophie; Moncho, Laura Cristina Trachsel; Rodgers, Samuel J; Singh, Sakshi & Ng, Matthew [Show all 9 contributors for this article] (2025). The RAB27A effector SYTL5 regulates mitophagy and mitochondrial metabolism. eLIFE. 14. doi: 10.7554/elife.105541. Full text in Research Archive Show summary

  SYTL5 is a member of the Synaptotagmin-Like (SYTL) protein family that differs from the Synaptotagmin family by having a unique N-terminal Synaptotagmin homology domain that directly interacts with the small GTPase RAB27A. Several SYTL protein family members have been implicated in plasma membrane transport and exocytosis, but the specific function of SYTL5 remains unknown. We here show that SYTL5 is a RAB27A effector and that both proteins localise to mitochondria and vesicles containing mitochondrial material. Mitochondrial recruitment of SYTL5 depends on its interaction with functional RAB27A. We demonstrate that SYTL5-RAB27A positive vesicles containing mitochondrial material, autophagy proteins and LAMP1 form during hypoxia and that depletion of SYTL5 and RAB27A reduces mitophagy under hypoxia mimicking conditions, indicating a role for these proteins in mitophagy. Indeed, we find that SYTL5 interacts with proteins involved in vesicle-mediated transport and cellular response to stress and that its depletion compromises mitochondrial respiration and increases glucose uptake. Intriguingly, SYTL5 expression is significantly reduced in tumours of the adrenal gland and correlates positively with survival for patients with adrenocortical carcinoma.

• Bouska, Alyssa; Zhang, Weiwei; Sharma, Sunandini; Holte, Harald; Shah, Rauf A. & Lone, Waseem G. [Show all 44 contributors for this article] (2025). Integrative Genomic and Transcriptomic Analysis Reveals Targetable Vulnerabilities in Angioimmunoblastic T‐Cell Lymphoma. American Journal of Hematology. ISSN 0361-8609. 100(9), p. 1486–1501. doi: 10.1002/ajh.27736. Full text in Research Archive Show summary

  Nodal follicular helper T‐cell ( T FH ) lymphoma of the angioimmunoblastic (AITL) subtype has a dismal prognosis. Using whole‐exome sequencing ( n  = 124), transcriptomic ( n  = 78), and methylation ( n  = 40) analysis, we identified recurrent mutations in known epigenetic drivers ( TET2, DNMT3A, IDH2 R172 ) and novel ones ( TET3, KMT2D ). TET2, IDH2 R172 , DNMT3A co‐mutated AITLs had poor prognosis ( p  < 0.0001). Genes regulating T‐cell receptor (TCR) signaling ( CD28, PLCG1, VAV1 , FYN ) or activation ( RHOA G17V ) or regulators of the PI3K‐pathway (PIK(3)C members, PTEN, PHLPP1, PHLPP2 ) were mutated. CD28 mutation/fusion was associated with poor prognosis ( p  = 0.02). WES of purified, neoplastic T‐cell (CD3 + PD1 + ) demonstrated high concordance with whole tumor biopsies and validated the presence of TET2 and DNMT3A in tumor and non‐lymphoid cells, but other mutations ( CD28 , RHOA G17V , IDH2 R172 , PLCG1 ) in neoplastic cells. Integrated DNA‐methylation and mRNA expression analysis revealed epigenetic alterations in genes regulating TCR, cytokines, PI3K‐signaling, and apoptosis. RNA‐seq analysis identified fusion transcripts regulating TCR‐activation (8%), revealed a restricted TCR‐repertoire (α = 87%, β = 72%), and showed the presence of Epstein–Barr virus transcriptome (73%). GEP demonstrated the association of B‐cells or dendritic cells in the tumor milieu with prognosis ( p  < 0.01). RNA‐seq and WES analysis of 12 AITL‐patient‐derived‐xenografts (PDX) showed that bi‐allelic TET2 and DNMT3A mutations or sub‐clonal mutations ( PLCG1, PHLPP2 ) propagated in sequential passages, and gene signatures related to T FH and T CM (central‐memory) were well‐maintained through passages. Gene expression signatures associated with late PDX passages (3rd–5th) were enriched with proliferation and metabolic reprogramming‐related genes and predicted prognosis in an independent AITL series. Low PHLPP2 mRNA expression predicted poor prognosis ( p  = 0.05) and engineered PHLPP2 or TET2 loss in CD4 + T‐cells showed enhanced PI(3)K activation, thus uncovering a therapeutic target for clinical trials.

• Wang, Shixiong; Santacana-Font, Gemma; Kedra, Darek; Gilfillan, Siv; Tena-Chaves, David & Bergholtz, Helga [Show all 13 contributors for this article] (2025). The harmonized activities of HER2–HER3 heterodimer and deacetylated FOXA1 evade hormone response by regulating FOXA1 chromatin binding. Nucleic Acids Research (NAR). ISSN 0305-1048. 53(20). doi: 10.1093/nar/gkaf1086. Full text in Research Archive Show summary

  FOXA1 is a key transcription factor that mediates the effects of estrogen receptor (ER) and HER2 signaling in breast cancer. However, the mechanisms underlying FOXA1 regulation by HER2 and ER remain poorly understood. Here, we investigated FOXA1 regulation in cells with varying HER2 levels and its impact on endocrine therapy response. Chromatin interaction analyses revealed that high HER2 levels enhance FOXA1 binding to chromatin regions while reducing ER occupancy. Mechanistically, FOXA1 is acetylated by the histone acetyltransferase EP300 at the WD1 domain in ER-positive cells, attenuating its DNA binding at HER2-induced chromatin regions. Conversely, FOXA1 deacetylation—triggered by HER2/HER3 activation—increases its binding to ER-independent regions and promotes insensitivity to hormone therapy. In a luminal breast cancer patient-derived xenograft model, HER2/HER3 signaling increased FOXA1 chromatin binding and reduced sensitivity to ER-targeted treatment. We identify HDAC2 as a key deacetylase modulating FOXA1 acetylation and partially mediating the effects of HER2/HER3 signaling. Altogether, our findings highlight the significance of FOXA1 acetylation, regulated by the HER2/HER3–HDAC2–FOXA1 axis, in controlling FOXA1 chromatin binding and shaping breast cancer progression and therapy response. These insights may inform future therapeutic strategies.

• Kanduri, Chakravarthi; Mamica, Maria Anna; Olstad, Emilie Willoch; Zucknick, Manuela; Jessica, Li, Jingyi & Sandve, Geir Kjetil Ferkingstad (2025). Beware of counter-intuitive levels of false discoveries in datasets with strong intra-correlations. Genome Biology. ISSN 1465-6906. 26. doi: 10.1186/s13059-025-03734-z. Full text in Research Archive Show summary

  The false discovery rate (FDR) controlling method by Benjamini and Hochberg (BH) is a popular choice in the omics fields. Here, we demonstrate that in datasets with a large degree of dependencies between features, FDR correction methods like BH can sometimes counter-intuitively report very high numbers of false positives, potentially misleading researchers. We call the attention of researchers to use suited multiple testing strategies and approaches like synthetic null data (negative control) to identify and minimize caveats related to false discoveries, as in the cases where false findings do occur, they may be numerous.

• Alwash, Mostafa; Hajj, Ghadi al; Grytten, Ivar & Sandve, Geir Kjetil Ferkingstad (2025). Meta simulation approach for evaluating machine learning method selection in data limited settings. Scientific Reports. 15. doi: 10.1038/s41598-025-24627-y. Full text in Research Archive Show summary

  Selecting appropriate machine learning&nbsp;(ML) methods for domain-specific tasks remains a persistent challenge, particularly in medicine where datasets are often small, heterogeneous, and incomplete. Traditional benchmarking strategies rely on limited observational samples, which may not capture the complexity of the underlying data-generating process&nbsp;(DGP). As a result, methods that perform well on available data may generalise poorly in real-world practice. We present SimCalibration, a meta-simulation framework that leverages structural learners&nbsp;(SLs) to infer an approximated data-generating process from limited data and generate synthetic datasets for large-scale benchmarking. This framework enables systematic evaluation of machine learning method selection strategies in settings where the true data-generating process is either known or can be approximated, allowing both validation against the ground truth and the generation of synthetic observations inferred from sparse samples. In rare disease research for example, where patient cohorts are inherently small, causal relationships are often conceptualised as directed acyclic graphs&nbsp;(DAGs). In this work, such structures are approximated directly from observational data, extending the utility of small datasets by enabling investigators to benchmark ML methods in a controlled simulation setting before deploying them in practice. This reduces the risk of selecting models that generalise poorly and supports more reliable decision-making in sensitive healthcare contexts. Experiments demonstrate that (a) structural learners vary in their ability to recover representative simulations for benchmarking, (b) structural learner-based benchmarking reduces variance in performance estimates compared to traditional validation, and (c) in some cases, structural learner-based approaches yield rankings that more closely match true relative performance than those derived from limited datasets. These findings highlight the value of simulation-based benchmarking for domains where drawing generalisable conclusions is critical, such as medicine, and offer greater transparency into the assumptions underlying predictive decisions.

• Ursu, Eugen; Minnegalieva, Aygul; Rawat, Puneet; Chernigovskaya, Maria; Tacutu, Robi & Sandve, Geir Kjetil Ferkingstad [Show all 8 contributors for this article] (2025). Training data composition determines machine learning generalization and biological rule discovery. Nature Machine Intelligence. 7(8), p. 1206–1219. doi: 10.1038/s42256-025-01089-5. Full text in Research Archive Show summary

  Supervised machine learning models depend on training datasets containing positive and negative examples: dataset composition directly impacts model performance and bias. Given the importance of machine learning for immunotherapeutic design, we examined how different negative class definitions affect model generalization and rule discovery for antibody–antigen binding. Using synthetic-structure-based binding data, we evaluated models trained with various definitions of negative sets. Our findings reveal that high out-of-distribution performance can be achieved when the negative dataset contains more similar samples to the positive dataset, despite lower in-distribution performance. Furthermore, by leveraging ground-truth information, we show that binding rules associated with positive data change based on the negative data used. Validation on experimental data supported simulation-based observations. This work underscores the role of dataset composition in creating robust, generalizable and biology-aware sequence-based ML models

• Chernigovskaya, Maria; Quy, Khang Le; Stensland, Maria; Singh, Sachin; Nelson, Rowan & Yilmaz, Melih [Show all 19 contributors for this article] (2025). Systematic benchmarking of mass spectrometry-based antibody sequencing reveals methodological biases. Cell Systems. ISSN 2405-4712. 16(11). doi: 10.1016/j.cels.2025.101449. Full text in Research Archive Show summary

  The circulating antibody (Ab) repertoire is crucial for immune protection, holding significant immunological and biotechnological value. While bottom-up mass spectrometry (MS) is widely used for profiling the sequence diversity of circulating Abs (Ab repertoire sequencing [Ab-seq]), it has not been thoroughly benchmarked. We quantified the replicability and robustness of Ab-seq using six monoclonal Ab spike-ins in 70 combinations of concentration and oligoclonality, with and without polyclonal serum immunoglobulin G (IgG) background. Each combination underwent four protease treatments and was analyzed across four experimental and three technical replicates, totaling 3,360 liquid chromatography-tandem MS (LC-MS/MS) runs. We quantified the dependence of Ab-seq identification on Ab sequence, concentration, protease, presence of background IgGs, and bioinformatics methods. Integrating the data from experimental replicates, proteases, and bioinformatics tools enhanced Ab identification. De novo sequencing performed similarly to database-dependent methods at higher Ab concentrations, but de novo Ab reconstruction remains challenging. Our work provides a foundational resource for the field of MS-based Ab profiling. A record of this paper’s transparent peer review process is included in the supplemental information

• George, Anne-Marie & Castillo, Ana Ozaki Rivera (2025). On Middle Grounds for Preference Statements. IJCAI International Joint Conference on Artificial Intelligence. ISSN 1045-0823. doi: 10.24963/ijcai.2025/503. Full text in Research Archive Show summary

  In group decisions or deliberations, stakeholders are often confronted with conflicting opinions. We investigate a logic-based way of expressing such opinions and a formal general notion of a middle ground between stakeholders. Inspired by the literature on preferences with hierarchical and lexicographic models, we instantiate our general framework to the case where stakeholders express their opinions using preference statements of the form ‘I prefer ‘a’ to ‘b’’, where ‘a’ and ‘b’ are alternatives expressed over some attributes, e.g., in a trolley problem, one can express I prefer to save 1 adult and 1 child to 2 adults (and 0 children). We prove theoretical results on the existence and uniqueness of middle grounds. In particular, we show that, for preference statements, middle grounds may not exist and may not be unique. We provide algorithms for deciding the existence and finding middle grounds.

• Trotter, James David; Ekmekçibaşı, Sinan; Sağbili, Doğan; Langguth, Johannes; Cai, Xing & Unat, Didem (2025). CPU- and GPU-initiated Communication Strategies for Conjugate Gradient Methods on Large GPU Clusters. In Diachin, Lori; Schulz, Martin & Randles, Amanda (Ed.), SC '25: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. Association for Computing Machinery (ACM). ISSN 9798400714665. p. 298–315. doi: 10.1145/3712285.3759774. Full text in Research Archive Show summary

  The Conjugate Gradient (CG) method is a key building block in numerous applications, yet its low computational intensity and sensitivity to communication overhead make it difficult to scale efficiently on multi-GPU systems. In light of recent advances in multi-GPU communication technologies, we revisit CG parallelization for large-scale GPU clusters. This work presents scalable CG and pipelined CG solvers targeting NVIDIA and AMD GPUs, using GPU-aware MPI, NCCL/RCCL and NVSHMEM to implement both CPU- and GPU-initiated communication schemes. We also introduce a monolithic variant that offloads the entire CG loop to the GPU, enabling fully device-initiated execution via NVSHMEM. Optimizations across all variants reduce unnecessary data transfers and synchronization overheads; the GPU-initiated variant eliminates CPU involvement altogether. We benchmark our implementations on NVIDIA- and AMD-based supercomputers using SuiteSparse matrices and a real-world finite element application. By avoiding data transfers and synchronization bottlenecks, our single-GPU implementations achieve 8–14 % performance gains over state-of-the-art solvers. In strong scaling tests on over 1,000 GPUs, we outperform existing approaches by 5–15 %. While CPU-initiated variants remain favorable due to a lack of vendor supported device-side computational kernels and suboptimal NVSHMEM configurations at the clusters, the strong scaling properties of the GPU-initiated CG variant indicates that it will be highly competitive at even larger GPU counts and with further tuning.

• Langguth, Johannes; Trotter, James David & Cai, Xing (2025). Modelling Load Imbalance In Shared Memory Multicore Systems. In Pericàs, Miquel & Ellsworth, Danielle (Ed.), Proceedings of the SC '25 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis. Association for Computing Machinery (ACM). ISSN 9798400718717. p. 1433–1441. doi: 10.1145/3731599.3767707. Full text in Research Archive Show summary

  Memory bandwidth has become the primary limiting factor of performance in many modern HPC applications, and it poses a limit to scalability because the achievable memory bandwidth only grows linearly with a small number of CPU cores. When the number of cores concurrently using the memory system exceeds a threshold, the aggregate memory bandwidth quickly saturates. To estimate the time usage of a computation dominated by memory traffic, the mainstream strategy is to divide the expected total memory traffic volume by the maximum memory bandwidth. However, this implicitly assumes homogeneous memory traffic which is often not the case, leading to inaccurate time estimates by the mainstream strategy. In this paper, we present a new performance model that specifically targets inhomogeneity in per-core memory traffic. The new requires only three hardware parameters. Using several cases of uneven per-core memory traffic, we demonstrate its advantage over the mainstream strategy.

• Athanasopoulos, Andreas; George, Anne-Marie & Dimitrakakis, Christos (2025). Probably Correct Optimal Stable Matching for Two-Sided Markets Under Uncertainty. In y Das, Sanmay; Nowé, Ann & Vorobeychik, Yevgeniy (Ed.), AAMAS '25: Proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems. Association for Computing Machinery (ACM). ISSN 9798400714269. p. 152–160. doi: https:/www.ifaamas.org/Proceedings/aamas2025/pdfs/p152.pdf. Full text in Research Archive Show summary

  We consider a learning problem for the stable marriage model under unknown preferences for the left side of the market. We focus on the centralized case, where at each time step, an online platform matches the agents and obtains a noisy evaluation reflecting their preferences. Our aim is to quickly identify the stable matching that is left-side optimal, rendering this a pure exploration problem with bandit feedback. We specifically aim to find Probably Correct Optimal Stable Matchings and present several bandit algorithms to do so. Our findings provide a foundational understanding of how to efficiently gather and utilize preference information to identify the optimal stable matching in two-sided markets under uncertainty. An experimental analysis on synthetic data complements theoretical results on sample complexities for the proposed methods.

• Riccardi, Enrico; Minotto, Thomas; Haff, Ingrid Hobæk & Sandve, Geir Kjetil Ferkingstad (2025). Connecting instance and model sparsity in multiple instance learning. Machine Learning with Applications (MLWA). 21, p. 100679–100679. doi: 10.1016/j.mlwa.2025.100679. Full text in Research Archive
• Minotto, Thomas; Haff, Ingrid Hobæk; Riccardi, Enrico & Sandve, Geir Kjetil Ferkingstad (2025). Detecting statistical interactions in immune receptor data: a comparative study. Journal of Applied Statistics. ISSN 0266-4763. p. 22–22. doi: 10.1080/02664763.2025.2533483. Full text in Research Archive
• Zhao, Sen; Nakken, Sigve; Vodak, Daniel & Hovig, Eivind (2025). FuSViz—visualization and interpretation of structural variation using cancer genomics and transcriptomics data. Nucleic Acids Research (NAR). ISSN 0305-1048. 53(4). doi: 10.1093/nar/gkaf078. Full text in Research Archive Show summary

  Structural variation (SV) is a frequent category of genetic alterations important for understanding cancer genome evolution and revealing key cancer driver events. With the development of high-throughput sequencing technologies, the ability to detect SVs of various sizes and types has improved, at both the DNA and RNA levels. However, SV calls are still prone to a considerable fraction of false positives, which necessitates visual inspection and manual curation as part of the quality control process. Identification of reliable and recurrent SVs in larger cohorts lends strength to revealing the driving roles of SVs in cancer development and to the discovery of potential diagnostic and prognostic biomarkers. Here, we present FuSViz, an application for visualization, interpretation, and prioritization of SVs. The tool provides multiple data view approaches in a user-friendly interface, allowing the investigation of prevalence and recurrence of SVs and relevant partner genes in a sample cohort. It integrates SV calls from DNA and RNA sequencing datasets to comprehensively illustrate the biological impact of SVs on the implicated genes and associated genomic regions. The functionality of FuSViz is intended for interrogation of both recurrent and private SVs, effectively assisting with pathogenicity evaluation and biomarker discovery in cancer sequencing projects.

• Yarar, Nurettin; Martens, Marvin; Rognes, Torbjørn; Lavender, Jan P.; Dirven, Hubert & Audouze, Karine [Show all 7 contributors for this article] (2025). AOP-networkFinder - a versatile tool for the reconstruction and visualization of Adverse Outcome Pathway networks from AOP-Wiki. Bioinformatics Advances. 5(1), p. 1–5. doi: 10.1093/bioadv/vbaf007. Full text in Research Archive Show summary

  Motivation The Adverse Outcome Pathways (AOP)-Wiki, a knowledge database for AOPs, requires an efficient way to present an overview of its content for the reconstruction of networks by experts in a given domain. We have developed the AOP-networkFinder, a user-friendly tool that retrieves AOPs of interest, allows network generation and cleaning, and finally visualizes networks built around the retrieved AOPs. Our tool constructs AOP networks by connecting AOPs that use the same Key Events (KEs) in a versatile but controlled manner. Genes related to these KEs are also displayed. The constructed networks can then be exported as images or to Cytoscape for further fine-tuning and statistical analysis. Results The AOP-networkFinder allows users to comprehensively identify relationships between KEs and visualize the overall structure of an AOP both quickly and easily. This is immensely beneficial to researchers who need to understand the complex interplay between different KEs and the overall pathway they are studying and helps them to build further networks of interest while logging relevant information about changes within the network. These efforts are in line with the Findable, Accessible, Interoperable, and Reusable principles, which are crucial attributes for any developed databases and tools for optimizing (re)use in a dynamically changing landscape of AOP-Wiki. Availability and implementation The AOP-networkFinder is an open-source application and is available online at aop-networkfinder.no, in the ‘Computational Toxicology at Norwegian Institute of Public Health’ Zenodo community at DOI 10.5281/zenodo.11068434, in the GitHub repository at github.com/folkehelseinstituttet/AOPnetworkFinder_v1, as well as in a Docker image at hub.docker.com/r/nurre123/aop_network_finder. The software is available under the GNU Affero General Public License (AGPL), v3.0. The tool uses the AOP-Wiki SPARQL endpoint to retrieve AOP data.

• Chernigovskaia, Maria; Pavlović, Milena; Kanduri, Chakravarthi; Gielis, Sofie; Robert, Philippe & Scheffer, Lonneke [Show all 12 contributors for this article] (2025). Simulation of adaptive immune receptors and repertoires with complex immune information to guide the development and benchmarking of AIRR machine learning. Nucleic Acids Research (NAR). ISSN 0305-1048. 53(3). doi: 10.1093/nar/gkaf025. Full text in Research Archive Show summary

  Machine learning (ML) has shown great potential in the adaptive immune receptor repertoire (AIRR) field. However, there is a lack of large-scale ground-truth experimental AIRR data suitable for AIRR-ML-based disease diagnostics and therapeutics discovery. Simulated ground-truth AIRR data are required to complement the development and benchmarking of robust and interpretable AIRR-ML methods where experimental data is currently inaccessible or insufficient. The challenge for simulated data to be useful is incorporating key features observed in experimental repertoires. These features, such as antigen or disease-associated immune information, cause AIRR-ML problems to be challenging. Here, we introduce LIgO, a software suite, which simulates AIRR data for the development and benchmarking of AIRR-ML methods. LIgO incorporates different types of immune information both on the receptor and the repertoire level and preserves native-like generation probability distribution. Additionally, LIgO assists users in determining the computational feasibility of their simulations. We show two examples where LIgO supports the development and validation of AIRR-ML methods: (i) how individuals carrying out-of-distribution immune information impacts receptor-level prediction performance and (ii) how immune information co-occurring in the same AIRs impacts the performance of conventional receptor-level encoding and repertoire-level classification approaches. LIgO guides the advancement and assessment of interpretable AIRR-ML methods.

• Stålberg, Stina Margrethe; Silwal-Pandit, Laxmi; Hamfjord, Julian; Nebdal, Daniel Johan Hammer; Lehtiö, J. & Lingjærde, Ole Christian [Show all 8 contributors for this article] (2025). Elevated KRAS protein level is associated with better survival in pancreatic cancer. BMC Cancer. 25. doi: 10.1186/s12885-025-14461-w. Full text in Research Archive Show summary

  Background Activating somatic KRAS mutations in hotspot loci occur almost universally (> 95%) in pancreatic ductal adenocarcinoma (PDAC). Both the presence of a KRAS mutation and high mRNA expression levels of KRAS in tumor tissue have been associated with worse outcome. Less is known about the expression of KRAS at the protein level and its association with clinical and molecular parameters. In the present study, we investigated the prognostic significance of the KRAS protein level and its relation to KRAS mutation status and the mRNA expression level. Methods A total of 41 PDAC tumors were screened for seven KRAS mutations (p.G12D, p.G12V, p.G12R, p.G12C, p.G12S, p.G13D, p.G12A) by the Wobble-enhanced ARMS method. Whole transcriptome and proteome profiles were obtained using mRNA microarrays (Agilent) and quantitative mass spectrometry-based proteomics (HiRIEF LC–MS/MS), respectively. The clinical outcome was overall survival (OS). Results KRAS mutations were identified in 88% of the tumors with p.G12D and p.G12V mutations being the most frequent. Tumors with p.G12V mutation had significantly higher KRAS mRNA expression than tumors with p.G12D, p.G12C, p.G12R or no mutation identified (P < 0.01). KRAS protein levels did not associate significantly to neither KRAS mRNA levels (Spearman’s rho = 0.18, P = 0.28) nor type of KRAS mutation. High KRAS protein level and mutation p.G12V were found to be significantly associated with better (P < 0.01) and worse OS (P < 0.05), respectively. Conclusions The KRAS protein level correlated poorly with KRAS mRNA expression level and was not significantly associated with the type of mutation present. Interestingly, we found that patients with high KRAS protein level in their tumors had a better clinical outcome.

• Johansen, Anette; Sandve, Geir Kjetil Ferkingstad; Maxwell, Joseph R.; Smithson, Glennda; Sollid, Ludvig Magne & Stamnæs, Jorunn (2024). Biopsy Proteome Score Performs Well as an Effect Measure in a Gluten Challenge Trial of Celiac Disease. Clinical Gastroenterology and Hepatology. ISSN 1542-3565. 23(5), p. 758–765.e8. doi: 10.1016/j.cgh.2024.08.005. Full text in Research Archive Show summary

  Background and Aims Development of novel treatments for celiac disease is dependent on precise tools to monitor changes in gluten-induced mucosal damage. Current histology measures are subjective and difficult to standardize. Biopsy proteome scoring is an objective alternative to histology which is based on robust changes in biological pathways that directly reflect gluten-induced mucosal damage. In this study, we aimed to evaluate biopsy proteome scoring as an effect measure in a clinical trial setting by measuring intestinal remodeling in response to oral gluten challenge. Methods We analyzed biopsies from a 14-day gluten challenge trial of treated celiac disease patients that consumed 3 g (n = 6) or 10 g (n = 7) gluten per day. Sections from individually embedded biopsies collected before and after challenge were processed for proteome scoring (n = 109) and measurement of villus height-to-crypt depth ratio (n = 58). Proteome scores were compared with histology, intraepithelial lymphocyte frequency and plasma interleukin-2. Results Change in proteome scores were significant for the group of patients who consumed 10 g gluten, but not for the group who consumed 3 g gluten. Altogether, 8 of 13 patients had changes in delta proteome scores above the cutoff. Proteome scores correlated with villus height-to-crypt depth ratios both at run-in and at day 15. Proteome scores at day 15 correlated with intraepithelial lymphocyte frequency and with plasma interleukin-2 levels measured 4 hours post–gluten intake. Conclusion Biopsy proteome scoring is a simple and reliable measure of gluten-induced mucosal remodeling in response to 14-day oral gluten challenge. ClinicalTrials.gov, Number: NCT03409796.

• Hajj, Ghadi al; Hubin, Aliaksandr; Kanduri, Chakravarthi; Pavlovic, Milena; Rand, Knut Dagestad & Widrich, Michael [Show all 11 contributors for this article] (2024). Incorporating probabilistic domain knowledge into deep multiple instance learning. Proceedings of Machine Learning Research (PMLR). 235, p. 17279–17297. Full text in Research Archive Show summary

  Deep learning methods, including deep multiple instance learning methods, have been criticized for their limited ability to incorporate domain knowledge. A reason that knowledge incorporation is challenging in deep learning is that the models usually lack a mapping between their model components and the entities of the domain, making it a non-trivial task to incorporate probabilistic prior information. In this work, we show that such a mapping between domain entities and model components can be defined for a multiple instance learning setting and propose a framework DeeMILIP that encompasses multiple strategies to exploit this mapping for prior knowledge incorporation. We motivate and formalize these strategies from a probabilistic perspective. Experiments on an immune-based diagnostics case show that our proposed strategies allow to learn generalizable models even in settings with weak signals, limited dataset size, and limited compute.

• Zhao, Max; Burchard, Luk Bjarne; Schroeder, Daniel Thilo; Langguth, Johannes & Cai, Xing (2024). iPuma: High-Performance Sequence Alignment on the Graphcore IPU. In Taufer, Michela; Hoefler, Torsten; Hammond, Jeff & Randles, Amanda (Ed.), ISC High Performance 2024 Research Paper Proceedings (39th International Conference). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9783982633602. doi: 10.23919/ISC.2024.10528941. Full text in Research Archive Show summary

  String alignment algorithms are an essential tool for understanding DNA and protein sequences. They demand substantial computation in real-world applications, and are thus a prime target for hardware acceleration. However, GPUs struggle to provide sufficient acceleration. Meanwhile, the recent MIMD-capable AI accelerators such as the Graphcore Intelligence Processing Unit (IPU) have become technologically viable. In this paper we present iPuma, a new implementation of Smith-Waterman sequence alignment for the IPU, which offers generalized short and medium length, one-to-one, and many-to-many high-throughput alignments for both DNA and protein sequences. iPuma is integrated into two bioinformatics pipelines, MetaHipMer2 and PASTIS. On protein datasets, iPuma shows speedups of 2.7x and 1.6x over state-of-the-art GPU and CPU implementations, respectively. We test the scalability on up to 64 IPUs, attaining a peak scoring performance of 1763 GCUPS for protein and 1168 GCUPS for DNA sequences.

• Wang, Xinyi; Muqeet, Asmar; Yue, Tao; Ali, Shaukat & Arcaini, Paolo (2024). Test Case Minimization with Quantum Annealers. ACM Transactions on Software Engineering and Methodology. ISSN 1049-331X. 34(1). doi: 10.1145/3680467. Full text in Research Archive Show summary

  Quantum annealers are specialized quantum computers for solving combinatorial optimization problems using special characteristics of quantum computing (QC), such as superposition, entanglement, and quantum tunneling. Theoretically, quantum annealers can outperform classical computers. However, the currently available quantum annealers are small-scale, i.e., they have limited quantum bits (qubits); hence, they currently cannot demonstrate the quantum advantage. Nonetheless, research is warranted to develop novel mechanisms to formulate combinatorial optimization problems for quantum annealing (QA). However, solving combinatorial problems with QA in software engineering remains unexplored. Toward this end, we propose BootQA, the very first effort at solving the test case minimization (TCM) problem with QA. In BootQA, we provide a novel formulation of TCM for QA, followed by devising a mechanism to incorporate bootstrap sampling to QA to optimize the use of qubits. We also implemented our TCM formulation in three other optimization processes: classical simulated annealing (SA), QA without problem decomposition, and QA with an existing D-Wave problem decomposition strategy, and conducted an empirical evaluation with three real-world TCM datasets. Results show that BootQA outperforms QA without problem decomposition and QA with the existing decomposition strategy in terms of effectiveness. Moreover, BootQA’s effectiveness is similar to SA. Finally, BootQA has higher efficiency in terms of time when solving large TCM problems than the other three optimization processes.

• Bogaard, Mari; Strømme, Jonas Meier; Kidd, Susanne Gundersen; Johannessen, Bjarne; Bakken, Anne Cathrine & Lothe, Ragnhild Adelheid [Show all 9 contributors for this article] (2024). GRIN3A: A biomarker associated with a cribriform pattern and poor prognosis in prostate cancer. Neoplasia. ISSN 1522-8002. 55. doi: 10.1016/j.neo.2024.101023. Full text in Research Archive Show summary

  Prostate cancer with a cribriform pattern, including invasive cribriform carcinoma (ICC) and/or intraductal carcinoma (IDC) is associated with a poor prognosis, and the underlying mechanisms are unclear. Therefore, we aimed to identify biomarkers for this feature. Using a radical prostatectomy cohort, we performed within-patient differential expression analyses with RNA sequencing data to compare samples with a cribriform pattern to those with non-cribriform Gleason pattern 4 (NcGP4; n=13). ACSM1, GRIN3A, PCDHB2, and REG4 were identified as differentially expressed, and validation was performed using real-time reverse transcription polymerase chain reaction (n=99; 321 RNA samples) and RNA in situ hybridization on tissue microarrays (n=479; 2047 tissue cores). GRIN3A was significantly higher expressed in cribriform pattern vs. NcGP4, when assessed within the same patient (n=27; p=0.005) and between different patients (n=83; p=0.001). Tissue cores with IDC more often expressed GRIN3A compared to ICC, NcGP4, and benign tissue (52 % vs. ≤ 32 %). When IDC and NcGP4 was compared within the same patient (173 pairs of tissue cores; 54 patients), 38 (22 %) of the tissue microarray core pairs had GRIN3A expression in only IDC, 33 (19 %) had expression in both IDC and NcGP4, 14 (8 %) in only NcGP4 and 88 (51 %) were negative in both entities (p=0.001). GRIN3A was as well associated with biochemical recurrence (log-rank, p=0.002). In conclusion, ectopic GRIN3A expression is an RNA-based biomarker for the presence of cribriform prostate cancer, particularly for IDC.

• Pavlović, Milena; Hajj, Ghadi al; Kanduri, Chakravarthi; Pensar, Johan; Wood, Mollie Elizabeth & Sollid, Ludvig Magne [Show all 8 contributors for this article] (2024). Improving generalization of machine learning-identified biomarkers using causal modelling with examples from immune receptor diagnostics. Nature Machine Intelligence. 6(1), p. 15–24. doi: 10.1038/s42256-023-00781-8. Full text in Research Archive
• Sveen, Anita; Johannessen, Bjarne; Klokkerud, Solveig Margrete Knoph; Kraggerud, Sigrid M.; Meza, Leonardo Zepeda & Bjørnslett, Merete Pauline [Show all 13 contributors for this article] (2024). Evolutionary mode and timing of dissemination of high-grade serous carcinomas. JCI Insight. 9:e170423(3), p. 1–19. doi: 10.1172/jci.insight.170423. Full text in Research Archive Show summary

  Dissemination within the peritoneal cavity is a main determinant of poor patient outcomes from high-grade serous carcinomas (HGSCs). The dissemination process is poorly understood from a cancer evolutionary perspective. We reconstructed the evolutionary trajectories across a median of 5 tumor sites and regions from each of 23 patients based on deep whole-exome sequencing. Polyclonal cancer origin was detected in 1 patient. Ovarian tumors had more complex subclonal architectures than other intraperitoneal tumors in each patient, which indicated that tumors developed earlier in the ovaries. Three common modes of dissemination were identified, including monoclonal or polyclonal dissemination of monophyletic (linear) or polyphyletic (branched) subclones. Mutation profiles of initial or disseminated clones varied greatly among cancers, but recurrent mutations were found in 7 cancer-critical genes, including TP53, BRCA1, BRCA2, and DNMT3A, and in the PI3K/AKT1 pathway. Disseminated clones developed late in the evolutionary trajectory models of most cancers, in particular in cancers with DNA damage repair deficiency. Polyclonal dissemination was predicted to occur predominantly as a single and rapid wave, but chemotherapy exposure was associated with higher genomic diversity of disseminated clones. In conclusion, we described three common evolutionary dissemination modes across HGSCs and proposed factors associated with dissemination diversity.

• Lång, Emma Helena; Lång, Anna Ulrika; Blicher, Pernille; Rognes, Torbjørn; Dommersnes, Paul Gunnar & Bøe, Stig Ove (2024). Topology-guided polar ordering of collective cell migration. Science Advances. 10(16), p. 1–14. doi: 10.1126/sciadv.adk4825. Full text in Research Archive Show summary

  The ability of epithelial monolayers to self-organize into a dynamic polarized state, where cells migrate in a uniform direction, is essential for tissue regeneration, development, and tumor progression. However, the mechanisms governing long-range polar ordering of motility direction in biological tissues remain unclear. Here, we investigate the self-organizing behavior of quiescent epithelial monolayers that transit to a dynamic state with long-range polar order upon growth factor exposure. We demonstrate that the heightened self-propelled activity of monolayer cells leads to formation of vortex-antivortex pairs that undergo sequential annihilation, ultimately driving the spread of long-range polar order throughout the system. A computational model, which treats the monolayer as an active elastic solid, accurately replicates this behavior, and weakening of cell-to-cell interactions impedes vortex-antivortex annihilation and polar ordering. Our findings uncover a mechanism in epithelia, where elastic solid material characteristics, activated self-propulsion, and topology-mediated guidance converge to fuel a highly efficient polar self-ordering activity.

• Stålberg, Stina Margrethe; Silwal-Pandit, Laxmi; Bastani, Nasser Ezzatkhah; Nebdal, Daniel J.H.; Lingjærde, Ole Christian & Skålhegg, Bjørn Steen [Show all 7 contributors for this article] (2024). Preoperative profiles of plasma amino acids and derivatives distinguish periampullary cancer and benign disease. BMC Cancer. 24(1). doi: 10.1186/s12885-024-12320-8. Full text in Research Archive Show summary

  Periampullary cancers, including pancreatic ductal adenocarcinoma, ampullary-, cholangio-, and duodenal carcinoma, are frequently diagnosed in an advanced stage and are associated with poor overall survival. They are difficult to differentiate from each other and challenging to distinguish from benign periampullary disease preoperatively. To improve the preoperative diagnostics of periampullary neoplasms, clinical or biological markers are warranted. In this study, 28 blood plasma amino acids and derivatives from preoperative patients with benign (N = 45) and malignant (N = 72) periampullary disease were analyzed by LC-MS/MS. Principal component analysis and consensus clustering both separated the patients with cancer and the patients with benign disease. Glutamic acid had significantly higher plasma expression and 15 other metabolites significantly lower plasma expression in patients with malignant disease compared with patients having benign disease. Phenylalanine was the only metabolite associated with improved overall survival (HR = 0.50, CI 0.30–0.83, P < 0.01). Taken together, plasma metabolite profiles from patients with malignant and benign periampullary disease were significantly different and have the potential to distinguish malignant from benign disease preoperatively.

• Johansen, Anette; Sandve, Geir Kjetil Ferkingstad; Ibsen, Jostein Holen; Lundin, Knut; Sollid, Ludvig Magne & Stamnæs, Jorunn (2024). Biopsy Proteome Scoring to Determine Mucosal Remodeling in Celiac Disease. Gastroenterology. ISSN 0016-5085. 167(3), p. 493–504. doi: 10.1053/j.gastro.2024.03.006. Full text in Research Archive Show summary

  Background & Aims Histologic evaluation of gut biopsies is a cornerstone for diagnosis and management of celiac disease (CeD). Despite its wide use, the method depends on proper biopsy orientation, and it suffers from interobserver variability. Biopsy proteome measurement reporting on the tissue state can be obtained by mass spectrometry analysis of formalin-fixed paraffin-embedded tissue. Here we aimed to transform biopsy proteome data into numerical scores that give observer-independent measures of mucosal remodeling in CeD. Methods A pipeline using glass-mounted formalin-fixed paraffin-embedded sections for mass spectrometry-based proteome analysis was established. Proteome data were converted to numerical scores using 2 complementary approaches: a rank-based enrichment score and a score based on machine learning using logistic regression. The 2 scoring approaches were compared with each other and with histology analyzing 18 patients with CeD with biopsies collected before and after treatment with a gluten-free diet as well as biopsies from patients with CeD with varying degree of remission (n = 22). Biopsies from individuals without CeD (n = 32) were also analyzed. Results The method yielded reliable proteome scoring of both unstained and H&E-stained glass-mounted sections. The scores of the 2 approaches were highly correlated, reflecting that both approaches pick up proteome changes in the same biological pathways. The proteome scores correlated with villus height-to-crypt depth ratio. Thus, the method is able to score biopsies with poor orientation. Conclusions Biopsy proteome scores give reliable observer and orientation-independent measures of mucosal remodeling in CeD. The proteomic method can readily be implemented by nonexpert laboratories in parallel to histology assessment and easily scaled for clinical trial settings.

• Reppe, Sjur; Gundersen, Sveinung; Sandve, Geir Kjetil Ferkingstad; Wang, Yunpeng; Andreassen, Ole A. & Medina-Gomez, Carolina [Show all 10 contributors for this article] (2024). Identification of Transcripts with Shared Roles in the Pathogenesis of Postmenopausal Osteoporosis and Cardiovascular Disease. International Journal of Molecular Sciences. ISSN 1661-6596. 25(10). doi: 10.3390/ijms25105554. Full text in Research Archive Show summary

  Epidemiological evidence suggests existing comorbidity between postmenopausal osteoporosis (OP) and cardiovascular disease (CVD), but identification of possible shared genes is lacking. The skeletal global transcriptomes were analyzed in trans-iliac bone biopsies (n=84) from clinically well-characterized postmenopausal women (50 to 86 years) using microchips and RNA sequencing. Thousand transcripts highly correlated with areal bone mineral density (aBMD) were further analyzed using bioinformatics, and common genes overlapping with CVD and their biological mechanisms, pathways and functions were identified. Fifty genes (45 mRNAs, 5 miRNAs) were discovered with established roles in oxidative stress, inflammatory response, endothelial function, fibrosis, dyslipidemia and osteoblastogenesis/calcification. These pleiotropic genes with possible CVD comorbidity functions were also present in transcriptomes of microvascular endothelial cells and cardiomyocytes and differentially expressed between healthy and osteoporotic women with fragility fractures. The results were supported by a genetic pleiotropy-informed conditional False Discovery Rate approach identifying overlap in single nucleotide polymorphisms (SNPs) within several genes encoding the aBMD and CVD associated transcripts.The study provides transcriptional and genomic evidence for genes of importance for both BMD regulation and CVD risk in a large collection of postmenopausal bone biopsies. Most of the transcripts identified in the CVD risk categories have no previously recognized roles in OP pathogenesis and provide novel avenues for exploring the mechanistic basis for the biological association between CVD and OP.

• Sæternes, Erik Hide; Thune, Andreas; Rustad, Alf Birger; Skeie, Tor & Cai, Xing (2024). Automated parameter tuning with accuracy control for efficient reservoir simulations. Journal of Computational Science. ISSN 1877-7503. 75. doi: 10.1016/j.jocs.2023.102205. Full text in Research Archive Show summary

  Computer simulations of complex physical processes typically require sophisticated numerical schemes that internally involve many parameters. Different choices of such internal numerical parameters may lead to considerably different levels of computational efficiency, some may even result in wrong simulation results. The task of finding an optimal set of the numerical parameters (e.g. for the purpose of minimising the simulation time), while ensuring an accepted level of numerical accuracy, is therefore extremely important but challenging. In this paper, we propose a new automated search algorithm that is based on constrained stochastic searches within the parameter space. This iterative search scheme is also equipped with an accuracy check, which adopts several complementary measures for quantifying the similarities between time series from different simulations, such that parameter choices that lead to insufficiently accurate results will be automatically rejected. As a concrete scenario of usage, we have applied the automated parameter search scheme to the open-source reservoir simulation framework OPM. An empirical study shows that a suitable design of the optimisation objective function, together with an appropriate choice of the number of trials per search iteration and the perturbation scale per trial, can produce fast and convergent improvements with respect to the optimisation objective. For example, for a set of 12 numerical parameters, 30 trials from five search iterations are sufficient for reducing the objective function by 30% for the open Norne black-oil reservoir model. The robustness of the automated search scheme is also demonstrated for two other open reservoir models. Moreover, it is found that the parameter values automatically identified for the Norne model can also greatly improve the simulation efficiency of another proprietary reservoir model that has drastically different scale, resolution and geological properties.

• Vu, Mai Ha; Robert, Philippe; Akbar, Rahmad; Swiatczak, Bartlomiej; Sandve, Geir Kjetil Ferkingstad & Haug, Dag Trygve Truslew [Show all 7 contributors for this article] (2024). Linguistics-based formalization of the antibody language as a basis for antibody language models. Nature Computational Science. 4, p. 412–422. doi: 10.1038/s43588-024-00642-3. Full text in Research Archive
• O'Donnell, Timothy J.; Kanduri, Chakravarthi; Isacchini, Giulio; Limenitakis, Julien P.; Brachman, Rebecca A. & Alvarez, Raymond A. [Show all 9 contributors for this article] (2024). Reading the repertoire: Progress in adaptive immune receptor analysis using machine learning. Cell Systems. ISSN 2405-4712. 15(12), p. 1168–1189. doi: 10.1016/j.cels.2024.11.006. Full text in Research Archive Show summary

  The adaptive immune system holds invaluable information on past and present immune responses in the form of B and T cell receptor sequences, but we are limited in our ability to decode this information. Machine learning approaches are under active investigation for a range of tasks relevant to understanding and manipulating the adaptive immune receptor repertoire, including matching receptors to the antigens they bind, generating antibodies or T cell receptors for use as therapeutics, and diagnosing disease based on patient repertoires. Progress on these tasks has the potential to substantially improve the development of vaccines, therapeutics, and diagnostics, as well as advance our understanding of fundamental immunological principles. We outline key challenges for the field, highlighting the need for software benchmarking, targeted large-scale data generation, and coordinated research efforts.

• Sharma, Abhibhav; Debik, Julia; Naume, Bjørn; Ohnstad, Hege Oma; Sahlberg, Guro Kristine Kleivi & Borgen, Elin [Show all 25 contributors for this article] (2024). Comprehensive multi-omics analysis of breast cancer reveals distinct long-term prognostic subtypes. Oncogenesis. 13(1), p. 1–13. doi: 10.1038/s41389-024-00521-6. Full text in Research Archive Show summary

  Breast cancer (BC) is a leading cause of cancer-related death worldwide. The diverse nature and heterogeneous biology of BC pose challenges for survival prediction, as patients with similar diagnoses often respond differently to treatment. Clinically relevant BC intrinsic subtypes have been established through gene expression profiling and are implemented in the clinic. While these intrinsic subtypes show a significant association with clinical outcomes, their long-term survival prediction beyond 5 years often deviates from expected clinical outcomes. This study aimed to identify naturally occurring long-term prognostic subgroups of BC based on an integrated multi-omics analysis. This study incorporates a clinical cohort of 335 untreated BC patients from the Oslo2 study with long-term follow-up (>12 years). Multi-Omics Factor Analysis (MOFA+) was employed to integrate transcriptomic, proteomic, and metabolomic data obtained from the tumor tissues. Our analysis revealed three prominent multi-omics clusters of BC patients with significantly different long-term prognoses (p = 0.005). The multi-omics clusters were validated in two independent large cohorts, METABRIC and TCGA. Importantly, a lack of prognostic association to long-term follow-up above 12 years in the previously established intrinsic subtypes was shown for these cohorts. Through a systems-biology approach, we identified varying enrichment levels of cell-cycle and immune-related pathways among the prognostic clusters. Integrated multi-omics analysis of BC revealed three distinct clusters with unique clinical and biological characteristics. Notably, these multi-omics clusters displayed robust associations with long-term survival, outperforming the established intrinsic subtypes.

• Bashour, Habib; Smorodina, Eva; Pariset, Matteo; Zhong, Jahn; Akbar, Rahmad & Chernigovskaia, Maria [Show all 15 contributors for this article] (2024). Biophysical cartography of the native and human-engineered antibody landscapes quantifies the plasticity of antibody developability. Communications Biology. 7(1), p. 1–25. doi: 10.1038/s42003-024-06561-3. Full text in Research Archive Show summary

  Designing effective monoclonal antibody (mAb) therapeutics faces a multi-parameter optimization challenge known as “developability”, which reflects an antibody’s ability to progress through development stages based on its physicochemical properties. While natural antibodies may provide valuable guidance for mAb selection, we lack a comprehensive understanding of natural developability parameter (DP) plasticity (redundancy, predictability, sensitivity) and how the DP landscapes of human-engineered and natural antibodies relate to one another. These gaps hinder fundamental developability profile cartography. To chart natural and engineered DP landscapes, we computed 40 sequence- and 46 structure-based DPs of over two million native and human-engineered single-chain antibody sequences. We find lower redundancy among structure-based compared to sequence-based DPs. Sequence DP sensitivity to single amino acid substitutions varied by antibody region and DP, and structure DP values varied across the conformational ensemble of antibody structures. We show that sequence DPs are more predictable than structure-based ones across different machine-learning tasks and embeddings, indicating a constrained sequence-based design space. Human-engineered antibodies localize within the developability and sequence landscapes of natural antibodies, suggesting that human-engineered antibodies explore mere subspaces of the natural one. Our work quantifies the plasticity of antibody developability, providing a fundamental resource for multi-parameter therapeutic mAb design.

• Rimehaug, Atle Eskeland; Dale, Anders M.; Arkhipov, Anton & Einevoll, Gaute Tomas (2024). Uncovering population contributions to the extracellular potential in the mouse visual system using Laminar Population Analysis. BioRxiv. ISSN 0362-4331. 20(12). doi: 10.1371/journal.pcbi.1011830. Full text in Research Archive
• Wang, Xinyi; Ali, Shaukat; Yue, Tao & Arcaini, Paolo (2024). Quantum Approximate Optimization Algorithm for Test Case Optimization. IEEE Transactions on Software Engineering. ISSN 0098-5589. 50(12), p. 3249–3264. doi: 10.1109/TSE.2024.3479421. Full text in Research Archive
• Minotto, Thomas; Robert, Philippe; Haff, Ingrid Hobæk & Sandve, Geir Kjetil Ferkingstad (2024). Assessing the feasibility of statistical inference using synthetic antibody-antigen datasets. Statistical Applications in Genetics and Molecular Biology. 23:20230027(1), p. 1–14. doi: 10.1515/sagmb-2023-0027. Full text in Research Archive Show summary

  Simulation frameworks are useful to stress-test predictive models when data is scarce, or to assert model sensitivity to specific data distributions. Such frameworks often need to recapitulate several layers of data complexity, including emergent properties that arise implicitly from the interaction between simulation components. Antibody-antigen binding is a complex mechanism by which an antibody sequence wraps itself around an antigen with high affinity. In this study, we use a synthetic simulation framework for antibody-antigen folding and binding on a 3D lattice that include full details on the spatial conformation of both molecules. We investigate how emergent properties arise in this framework, in particular the physical proximity of amino acids, their presence on the binding interface, or the binding status of a sequence, and relate that to the individual and pairwise contributions of amino acids in statistical models for binding prediction. We show that weights learnt from a simple logistic regression model align with some but not all features of amino acids involved in the binding, and that predictive sequence binding patterns can be enriched. In particular, main effects correlated with the capacity of a sequence to bind any antigen, while statistical interactions were related to sequence specificity.

• Yang, Mingyi; Kaarbø, Mari; Myhre, Vegard; Reims, Henrik Mikael; Karlsen, Tom Hemming & Wang, Junbai [Show all 13 contributors for this article] (2024). Altered Genome-Wide DNA Methylation in the Duodenum of Common Variable Immunodeficiency Patients. Journal of Clinical Immunology. ISSN 0271-9142. 44(6), p. 1–16. doi: 10.1007/s10875-024-01726-5. Full text in Research Archive Show summary

  Purpose A large proportion of Common variable immunodeficiency (CVID) patients has duodenal inflammation with increased intraepithelial lymphocytes (IEL) of unknown aetiology. The histologic similarities to celiac disease, lead to confusion regarding treatment (gluten-free diet) of these patients. We aimed to elucidate the role of epigenetic DNA methylation in the aetiology of duodenal inflammation in CVID and differentiate it from true celiac disease. Methods DNA was isolated from snap-frozen pieces of duodenal biopsies and analysed for differences in genome-wide epigenetic DNA methylation between CVID patients with increased IEL (CVID_IEL; n = 5) without IEL (CVID_N; n = 3), celiac disease (n = 3) and healthy controls (n = 3). Results The DNA methylation data of 5-methylcytosine in CpG sites separated CVID and celiac diseases from healthy controls. Differential methylation in promoters of genes were identified as potential novel mediators in CVID and celiac disease. There was limited overlap of methylation associated genes between CVID_IEL and Celiac disease. High frequency of differentially methylated CpG sites was detected in over 100 genes nearby transcription start site (TSS) in both CVID_IEL and celiac disease, compared to healthy controls. Differential methylation of genes involved in regulation of TNF/cytokine production were enriched in CVID_IEL, compared to healthy controls. Conclusion This is the first study to reveal a role of epigenetic DNA methylation in the etiology of duodenal inflammation of CVID patients, distinguishing CVID_IEL from celiac disease. We identified potential biomarkers and therapeutic targets within gene promotors and in high-frequency differentially methylated CpG regions proximal to TSS in both CVID_IEL and celiac disease.

• Hsieh, Ping-Han; Hsiao, Ru-Xiu; Ferenc, Katalin; Mathelier, Anthony; Burkholz, Rebekka & Chen, Chien-Yu [Show all 9 contributors for this article] (2024). CAVACHON: a hierarchical variational autoencoder to integrate multi-modal single-cell data. arXiv.org. ISSN 2331-8422. doi: 10.48550/arXiv.2405.18655. Full text in Research Archive
• Skotheim, Rolf I.; Bogaard, Mari; Carm, Kristina Totland; Axcrona, Ulrika & Axcrona, Karol (2024). Prostate cancer: Molecular aspects, consequences, and opportunities of the multifocal nature. Biochimica et Biophysica Acta. CR. Reviews on Cancer. ISSN 0304-419X. 1879(2). doi: 10.1016/j.bbcan.2024.189080. Full text in Research Archive
• Andresen, Nikolai Kragøe; Røssevold, Andreas Hagen; Quaghebeur, Claire; Gilje, Bjørnar; Boge, Beate & Gombos, Andrea [Show all 17 contributors for this article] (2024). Ipilimumab and nivolumab combined with anthracycline-based chemotherapy in metastatic hormone receptor-positive breast cancer: A randomized phase 2b trial. Journal for ImmunoTherapy of Cancer (JITC). 12:e007990(1), p. 1–12. doi: 10.1136/jitc-2023-007990. Full text in Research Archive Show summary

  Background Immune checkpoint inhibitors have shown minimal clinical activity in hormone receptor-positive metastatic breast cancer (HR+ mBC). Doxorubicin and low- dose cyclophosphamide are reported to induce immune responses and counter regulatory T cells (Tregs). Here, we report the efficacy and safety of combined programmed cell death protein-1/cytotoxic T-lymphocyte- associated protein 4 blockade concomitant with or after immunomodulatory chemotherapy for HR + mBC. Methods Patients with HR+ mBC starting first-/second- line chemotherapy (chemo) were randomized 2:3 to chemotherapy (pegylated liposomal doxorubicin 20 mg/ m 2 every second week plus cyclophosphamide 50 mg by mouth/day in every other 2-week cycle) with or without concomitant ipilimumab (ipi; 1 mg/kg every sixth week) and nivolumab (nivo; 240 mg every second week). Patients in the chemo-only arm were offered cross-over to ipi/ nivo without chemotherapy. Co-primary endpoints were safety in all patients starting therapy and progression-free survival (PFS) in the per-protocol (PP) population, defined as all patients evaluated for response and receiving at least two treatment cycles. Secondary endpoints included objective response rate, clinical benefit rate, Treg changes during therapy and assessment of programmed death- ligand 1 (PD-L1), mutational burden and immune gene signatures as biomarkers. Results Eighty-two patients were randomized and received immune-chemo (N=49) or chemo-only (N=33), 16 patients continued to the ipi/nivo-only cross- over arm. Median follow-up was 41.4 months. Serious adverse events occurred in 63% in the immune-chemo arm, 39% in the chemo-only arm and 31% in the cross- over-arm. In the PP population (N=78) median PFS in the immune- chemo arm was 5.1 months, compared with 3.6 months in the chemo-only arm, with HR 0.94 (95% CI 0.59 to 1.51). Clinical benefit rates were 55% (26/47) and 48% (15/31) in the immune-chemo and chemo- only arms, respectively. In the cross-over-arm (ipi/nivo-only), objective responses were observed in 19% of patients (3/16) and clinical benefit in 25% (4/16). Treg levels in blood decreased after study chemotherapy. High-grade immune-related adverse events were associated with prolonged PFS. PD-L1 status and mutational burden were not associated with ipi/nivo benefit, whereas a numerical PFS advantage was observed for patients with a high Treg gene signature in tumor. Conclusion The addition of ipi/nivo to chemotherapy increased toxicity without improving efficacy. Ipi/nivo administered sequentially to chemotherapy was tolerable and induced clinical responses.

• Scheffer, Lonneke; Reber, Eric Emanuel; Mehta, Brij Bhushan; Pavlović, Milena; Chernigovskaia, Maria & Richardson, Eve [Show all 11 contributors for this article] (2024). Predictability of antigen binding based on short motifs in the antibody CDRH3. Briefings in Bioinformatics. ISSN 1467-5463. 25(6), p. 1–10. doi: 10.1093/bib/bbae537. Full text in Research Archive Show summary

  Adaptive immune receptors, such as antibodies and T-cell receptors, recognize foreign threats with exquisite specificity. A major challenge in adaptive immunology is discovering the rules governing immune receptor–antigen binding in order to predict the antigen binding status of previously unseen immune receptors. Many studies assume that the antigen binding status of an immune receptor may be determined by the presence of a short motif in the complementarity determining region 3 (CDR3), disregarding other amino acids. To test this assumption, we present a method to discover short motifs which show high precision in predicting antigen binding and generalize well to unseen simulated and experimental data. Our analysis of a mutagenesis-based antibody dataset reveals 11 336 position-specific, mostly gapped motifs of 3–5 amino acids that retain high precision on independently generated experimental data. Using a subset of only 178 motifs, a simple classifier was made that on the independently generated dataset outperformed a deep learning model proposed specifically for such datasets. In conclusion, our findings support the notion that for some antibodies, antigen binding may be largely determined by a short CDR3 motif. As more experimental data emerge, our methodology could serve as a foundation for in-depth investigations into antigen binding signals.

• Bai, Baoyan; Wise, Jillian Frances; Vodak, Daniel; Nakken, Sigve; Sharma, Ankush & Blaker, Yngvild Nuvin [Show all 23 contributors for this article] (2024). Multi-omics profiling of longitudinal samples reveals early genomic changes in follicular lymphoma. Blood Cancer Journal. 14:147(1), p. 1–12. doi: 10.1038/s41408-024-01124-5. Full text in Research Archive Show summary

  Follicular lymphoma (FL) is the most common indolent type of B-cell non-Hodgkin lymphoma. Advances in treatment have improved overall survival, but early relapse or transformation to aggressive disease is associated with inferior outcome. To identify early genetic events and track tumor clonal evolution, we performed multi-omics analysis of 94 longitudinal biopsies from 44 FL patients; 22 with transformation (tFL) and 22 with relapse without transformation (nFL). Deep whole-exome sequencing confirmed recurrent mutations in genes encoding epigenetic regulators (CREBBP, KMT2D, EZH2, EP300), with similar mutational landscape in nFL and tFL patients. Calculation of genomic distances between longitudinal samples revealed complex evolutionary patterns in both subgroups. CREBBP and KMT2D mutations were identified as genetic events that occur early in the disease course, and cases with CREBBP KAT domain mutations had low risk of transformation. Gains in chromosomes 12 and 18 (TCF4), and loss in 6q were identified as early and stable copy number alterations. Identification of such early and stable genetic events may provide opportunities for early disease detection and disease monitoring. Integrative analysis revealed that tumors with EZH2 mutations exhibited reduced gene expression of numerous histone genes, including histone linker genes. This might contribute to the epigenetic dysregulation in FL.

• Lund-Andersen, Christin; Kristensen, Annette Torgunrud; Kanduri, Chakravarthi; Dagenborg, Vegar Johansen; Frøysnes, Ida Storhaug & Larsen, Mette Mellbye [Show all 9 contributors for this article] (2024). Novel drug resistance mechanisms and drug targets in BRAF-mutated peritoneal metastasis from colorectal cancer. Journal of Translational Medicine. 22:646(1), p. 1–15. doi: 10.1186/s12967-024-05467-2. Full text in Research Archive Show summary

  Background Patients with peritoneal metastasis from colorectal cancer (PM-CRC) have inferior prognosis and respond particularly poorly to chemotherapy. This study aims to identify the molecular explanation for the observed clinical behavior and suggest novel treatment strategies in PM-CRC. Methods Tumor samples (230) from a Norwegian national cohort undergoing surgery and hyperthermic intraperitoneal chemotherapy (HIPEC) with mitomycin C (MMC) for PM-CRC were subjected to targeted DNA sequencing, and associations with clinical data were analyzed. mRNA sequencing was conducted on a subset of 30 samples to compare gene expression in tumors harboring BRAF or KRAS mutations and wild-type tumors. Results BRAF mutations were detected in 27% of the patients, and the BRAF-mutated subgroup had inferior overall survival compared to wild-type cases (median 16 vs 36 months, respectively, p < 0.001). BRAF mutations were associated with RNF43/RSPO aberrations and low expression of negative Wnt regulators (ligand-dependent Wnt activation). Furthermore, BRAF mutations were associated with gene expression changes in transport solute carrier proteins (specifically SLC7A6) and drug metabolism enzymes (CES1 and CYP3A4) that could influence the efficacy of MMC and irinotecan, respectively. BRAF-mutated tumors additionally exhibited increased expression of members of the novel butyrophilin subfamily of immune checkpoint molecules (BTN1A1 and BTNL9). Conclusions BRAF mutations were frequently detected and were associated with particularly poor survival in this cohort, possibly related to ligand-dependent Wnt activation and altered drug transport and metabolism that could confer resistance to MMC and irinotecan. Drugs that target ligand-dependent Wnt activation or the BTN immune checkpoints could represent two novel therapy approaches.

• Strøm, Thea Bismo; Asprusten, Emil Andreas; Lærdahl, Jon Kristen; Øygard, Irene Waal; Hussain, M. Mahmood & Bogsrud, Martin Prøven [Show all 7 contributors for this article] (2023). Missense mutation Q384K in the APOB gene affecting the large lipid transfer module of apoB reduces the secretion of apoB-100 in the liver without reducing the secretion of apoB-48 in the intestine. Journal of Clinical Lipidology. ISSN 1933-2874. 17(6), p. 800–807. doi: 10.1016/j.jacl.2023.08.009. Full text in Research Archive Show summary

  Background Molecular genetic testing of patients with hypobetalipoproteinemia may identify a genetic cause that can form the basis for starting proper therapy. Identifying a genetic cause may also provide novel data on the structure-function relationship of the mutant protein. Objective To identify a genetic cause of hypobetalipoproteinemia in a patient with levels of low density lipoprotein cholesterol at the detection limit of 0.1 mmol/l. Methods DNA sequencing of the translated exons with flanking intron sequences of the genes adenosine triphosphate-binding cassette transporter 1, angiopoietin-like protein 3, apolipoprotein B, apolipoprotein A1, lecithin-cholesterol acyltransferase, microsomal triglyceride transfer protein and proprotein convertase subtilisin/kexin type 9. Results The patient was homozygous for mutation Q384K (c.1150C>A) in the apolipoprotein B gene, and this mutation segregated with hypobetalipoproteinemia in the family. Residue Gln384 is located in the large lipid transfer module of apoB that has been suggested to be important for lipidation of apolipoprotein B through interaction with microsomal triglyceride transfer protein. Based on measurements of serum levels of triglycerides and apolipoprotein B-48 after an oral fat load, we conclude that the patient was able to synthesize apolipoprotein B-48 in the intestine in a seemingly normal fashion. Conclusion Our data indicate that mutation Q384K severely reduces the secretion of apolipoprotein B-100 in the liver without reducing the secretion of apolipoprotein B-48 in the intestine. Possible mechanisms for the different effects of this and other missense mutations affecting the large lipid transfer module on the two forms of apoB are discussed.

• Kanduri, Chakravarthi; Scheffer, Lonneke; Pavlović, Milena; Rand, Knut Dagestad; Chernigovskaia, Maria & Pirvandy, Oz [Show all 9 contributors for this article] (2023). simAIRR: simulation of adaptive immune repertoires with realistic receptor sequence sharing for benchmarking of immune state prediction methods. GigaScience. 12, p. 1–16. doi: 10.1093/gigascience/giad074. Full text in Research Archive Show summary

  Abstract Background Machine learning (ML) has gained significant attention for classifying immune states in adaptive immune receptor repertoires (AIRRs) to support the advancement of immunodiagnostics and therapeutics. Simulated data are crucial for the rigorous benchmarking of AIRR-ML methods. Existing approaches to generating synthetic benchmarking datasets result in the generation of naive repertoires missing the key feature of many shared receptor sequences (selected for common antigens) found in antigen-experienced repertoires. Results We demonstrate that a common approach to generating simulated AIRR benchmark datasets can introduce biases, which may be exploited for undesired shortcut learning by certain ML methods. To mitigate undesirable access to true signals in simulated AIRR datasets, we devised a simulation strategy (simAIRR) that constructs antigen-experienced-like repertoires with a realistic overlap of receptor sequences. simAIRR can be used for constructing AIRR-level benchmarks based on a range of assumptions (or experimental data sources) for what constitutes receptor-level immune signals. This includes the possibility of making or not making any prior assumptions regarding the similarity or commonality of immune state–associated sequences that will be used as true signals. We demonstrate the real-world realism of our proposed simulation approach by showing that basic ML strategies perform similarly on simAIRR-generated and real-world experimental AIRR datasets. Conclusions This study sheds light on the potential shortcut learning opportunities for ML methods that can arise with the state-of-the-art way of simulating AIRR datasets. simAIRR is available as a Python package: https://github.com/KanduriC/simAIRR.

• Olstad, Emilie Willoch; Nordeng, Hedvig Marie Egeland; Sandve, Geir Kjetil Ferkingstad; Lyle, Robert & Gervin, Kristina (2023). Effects of prenatal exposure to (es)citalopram and maternal depression during pregnancy on DNA methylation and child neurodevelopment. Translational Psychiatry. 13(1). doi: 10.1038/s41398-023-02441-2. Full text in Research Archive Show summary

  Abstract Studies assessing associations between prenatal exposure to antidepressants, maternal depression, and offspring DNA methylation (DNAm) have been inconsistent. Here, we investigated whether prenatal exposure to citalopram or escitalopram ((es)citalopram) and maternal depression is associated with differences in DNAm. Then, we examined if there is an interaction effect of (es)citalopram exposure and DNAm on offspring neurodevelopmental outcomes. Finally, we investigated whether DNAm at birth correlates with neurodevelopmental trajectories in childhood. We analyzed DNAm in cord blood from the Norwegian Mother, Father and Child Cohort Study (MoBa) biobank. MoBa contains questionnaire data on maternal (es)citalopram use and depression during pregnancy and information about child neurodevelopmental outcomes assessed by internationally recognized psychometric tests. In addition, we retrieved ADHD diagnoses from the Norwegian Patient Registry and information on pregnancies from the Medical Birth Registry of Norway. In total, 958 newborn cord blood samples were divided into three groups: (1) prenatal (es)citalopram exposed ( n  = 306), (2) prenatal maternal depression exposed ( n  = 308), and (3) propensity score-selected controls ( n  = 344). Among children exposed to (es)citalopram, there were more ADHD diagnoses and symptoms and delayed communication and psychomotor development. We did not identify differential DNAm associated with (es)citalopram or depression, nor any interaction effects on neurodevelopmental outcomes throughout childhood. Trajectory modeling identified subgroups of children following similar developmental patterns. Some of these subgroups were enriched for children exposed to maternal depression, and some subgroups were associated with differences in DNAm at birth. Interestingly, several of the differentially methylated genes are involved in neuronal processes and development. These results suggest DNAm as a potential predictive molecular marker of later abnormal neurodevelopmental outcomes, but we cannot conclude whether DNAm links prenatal (es)citalopram exposure or maternal depression with child neurodevelopmental outcomes.

• Kalyanasundaram, Sumana; Lefol, Yohan Pierre; Gundersen, Sveinung; Rognes, Torbjørn; Alsøe, Lene & Nilsen, Hilde [Show all 9 contributors for this article] (2023). hGSuite HyperBrowser: A web-based toolkit for hierarchical metadata-informed analysis of genomic tracks. PLOS ONE. 18(7). doi: 10.1371/journal.pone.0286330. Full text in Research Archive Show summary

  Many high-throughput sequencing datasets can be represented as objects with coordinates along a reference genome. Currently, biological investigations often involve a large number of such datasets, for example representing different cell types or epigenetic factors. Drawing overall conclusions from a large collection of results for individual datasets may be challenging and time-consuming. Meaningful interpretation often requires the results to be aggregated according to metadata that represents biological characteristics of interest. In this light, we here propose the hierarchical Genomic Suite HyperBrowser (hGSuite), an open-source extension to the GSuite HyperBrowser platform, which aims to provide a means for extracting key results from an aggregated collection of high-throughput DNA sequencing data. The hGSuite utilizes a metadata-informed data cube to calculate various statistics across the multiple dimensions of the datasets. With this work, we show that the hGSuite and its associated data cube methodology offers a quick and accessible way for exploratory analysis of large genomic datasets. The web-based toolkit named hGsuite Hyperbrowser is available at https://hyperbrowser.uio.no/hgsuite under a GPLv3 license.

• Shakibfar, Saeed; Nyberg, Fredrik; Li, Huiqi; Zhao, Jing; Nordeng, Hedvig Marie Egeland & Sandve, Geir Kjetil Ferkingstad [Show all 10 contributors for this article] (2023). Artificial intelligence-driven prediction of COVID-19-related hospitalization and death: a systematic review. Frontiers in Public Health. 11. doi: 10.3389/fpubh.2023.1183725. Full text in Research Archive Show summary

  Aim To perform a systematic review on the use of Artificial Intelligence (AI) techniques for predicting COVID-19 hospitalization and mortality using primary and secondary data sources. Study eligibility criteria Cohort, clinical trials, meta-analyses, and observational studies investigating COVID-19 hospitalization or mortality using artificial intelligence techniques were eligible. Articles without a full text available in the English language were excluded. Data sources Articles recorded in Ovid MEDLINE from 01/01/2019 to 22/08/2022 were screened. Data extraction We extracted information on data sources, AI models, and epidemiological aspects of retrieved studies. Bias assessment A bias assessment of AI models was done using PROBAST. Participants Patients tested positive for COVID-19. Results We included 39 studies related to AI-based prediction of hospitalization and death related to COVID-19. The articles were published in the period 2019-2022, and mostly used Random Forest as the model with the best performance. AI models were trained using cohorts of individuals sampled from populations of European and non-European countries, mostly with cohort sample size &amp;lt;5,000. Data collection generally included information on demographics, clinical records, laboratory results, and pharmacological treatments (i.e., high-dimensional datasets). In most studies, the models were internally validated with cross-validation, but the majority of studies lacked external validation and calibration. Covariates were not prioritized using ensemble approaches in most of the studies, however, models still showed moderately good performances with Area under the Receiver operating characteristic Curve (AUC) values &amp;gt;0.7. According to the assessment with PROBAST, all models had a high risk of bias and/or concern regarding applicability. Conclusions A broad range of AI techniques have been used to predict COVID-19 hospitalization and mortality. The studies reported good prediction performance of AI models, however, high risk of bias and/or concern regarding applicability were detected.

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• Haraldsrud, Andreas & Sundnes, Joakim (2025). Programmering for naturvitenskap og matematikk. Universitetsforlaget. ISBN 9788215066646. 539 p. Full text in Research Archive

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• Tran, Thi Thuy Chau; Erdem, Cagri; Yazidi, Anis; Kjølerbakken, Kai Morgan & Griwodz, Carsten (2026). Research Proposal: Non-intrusive Stress Recognition using Multimodality Deep Learning. doi: 10.1145/3793853.3798410. Full text in Research Archive
• Pollock, Joanna Katharine Mary & Sandve, Geir Kjetil Ferkingstad (2026). Diskusjon om kunstig intelligens i forskning. Full text in Research Archive
• Melteig, Elina; Glad, Ingrid Kristine; Ramezani-Kebrya, Ali; Pérez, Johan Manuel de Aguas; Holmsen, Sigurd & Palomares, Alfonso Diz-Lois [Show all 7 contributors for this article] (2026). Blir KI vår dommedag? Ekspertene strides. [Journal]. Titan. Full text in Research Archive
• Ramezani-Kebrya, Ali (2026). Information Relevance and Uncertainty Quantification. Full text in Research Archive
• Ramezani-Kebrya, Ali (2026). Information Relevance and Uncertatnty Quantification. Full text in Research Archive
• Ramezani-Kebrya, Ali (2026). Machine Learning in Real World. Full text in Research Archive
• Zuccato, Lorenzo; George, Anne-Marie; Vitelli, Valeria; Frigessi, Arnoldo; Giese, Martin & Scheel, Ida (2026). Bridging Statistical and Logical Perspectives on Inconsistency. Full text in Research Archive
• Jensenius, Alexander Refsum; Sandve, Geir Kjetil Ferkingstad; Boasson, Elin Lerum; Mjaaland, Marius Timmann; Skjelkvåle, Brit Lisa & Moen, Kåre [Show all 7 contributors for this article] (2026). What can the Centre for Global Sustainability do for UiO – and the other way around? Full text in Research Archive
• Malthe-Sørenssen, Anders; Harang, Fabian Andsem; Johnson, Victoria Christine Årang; Nyrup, Jacob; Lie, Knut-Andreas & Sandve, Geir Kjetil Ferkingstad [Show all 7 contributors for this article] (2026). Hvordan endrer KI-agenter forskningsprosessen? Full text in Research Archive Show summary

  Dette møtet vil ta for seg de forskningsetiske spørsmålene som reiser seg i møte med bruken av KI-agenter i forskning

• Jemterud, Torkild; Roberts, Aubrey Jane; Sandve, Geir Kjetil Ferkingstad & Vestre, Katharina (2026). Hva er greia med hestesko? - Abels tårn - NRK Radio. [Radio]. NRK. Full text in Research Archive Show summary

  Når vet en stamcelle hva den skal bli? Hvordan belønne KI? Er jeg mer av min mormor enn av min mor? Hadde dinosaurene pandemier? Hvor fargeblinde er hunder?

• Axcrona, Karol; Axcrona, Ulrika & Skotheim, Rolf I. (2025). Re: Histopathologic features and transcriptomic signatures do not solve the issue of magnetic resonance imaging-invisible prostate cancers: A matched-pair analysis. European Urology. ISSN 0302-2838. 88(2), p. 215–216. doi: 10.1016/j.eururo.2025.04.002. Full text in Research Archive Show summary

  Oderda M, Marquis A, Bertero L, et al Prostate 2025; 85: 374–84 Experts’ summary: Oderda et al. explore the histopathological and transcriptomic aspects of prostate cancer (PC) that remain undetectable via multiparametric magnetic resonance imaging (mpMRI). Their retrospective matched-pair cohort analysis focused on patients treated with radical prostatectomy for clinically significant PC and compared groups with mpMRI-invisible tumors (negative preoperative mpMRI) versus mpMRI-visible tumors. Key clinical parameters, such as prostate-specific antigen levels, prostate volume, and International Society of Urological Pathology grade group, were consistent between the groups. The study involved radiological and pathological review and examination of 780 gene transcripts across various signaling pathways and cell-type profiling. Notably, while there were no significant differences in histopathological features or gene expression profiles between the groups, specific subgroups of mpMRI-invisible cancers showed upregulation in pathways associated with cell adhesion, motility, extracellular matrix remodeling, and metastatic processes. The authors conclude that no pathological or gene-expression PC hallmarks explaining mpMRI invisibility were identified. Aggressive features can be present in both mpMRI-invisible and mpMRI-visible tumors. Experts’ comments: While mpMRI has become standard in PC diagnosis [1], this paper highlights that detection challenges remain, with significant cancers going undetected. An ideal biomarker would be informative regardless of whether the most significant prostate area is sampled, and perhaps even for samples from normal-appearing prostate regions. However, there is a lack of appropriate data sets with omics profiling data from multiple sites and multiple tumor foci, which is important because PC is a multifocal disease with extensive interfocal and intrafocal heterogeneity [2]. Molecular attempts have been made to identify biomarkers that are homogeneously expressed across the prostate and that yield the same analytical results for biopsies from different tumor foci [3]. MRI findings inform decisions regarding active surveillance versus radical treatment for patients with low- to intermediate-risk PC. There is a need for better molecular risk stratification to investigate implementation of biomarkers to guide treatment [4] and potentially to facilitate molecular classification of the index tumor [5]. These biomarkers should ideally be investigated in prostate biopsies from men undergoing investigation for PC. Taken together with the multifocal nature of PC, the results reported by Oderda et al point to a need for more thorough primary investigations for men with suspicion of PC. We foresee that men with PC being considered for active surveillance may benefit from genomic testing to identify gene signatures that would result in poorer prognosis.

• Zobolas, John; George, Anne-Marie; López, Alberto; Fischer, Sebastian; Becker, Marc & Aittokallio, Tero Antero (2025). A Pareto-Driven Ensemble Feature Selection Approach Optimizes Biomarker Discovery in Multi-omics Pancreatic Cancer Studies. Full text in Research Archive Show summary

  Prediction of patient survival using high-dimensional multi-omics data requires systematic feature selection methods that ensure predictive performance, sparsity, and reliability for prognostic biomarker discovery. We developed a hybrid ensemble feature selection (hEFS) approach that combines data subsampling with multiple prognostic models, integrating both embedded and wrapper-based strategies for survival prediction. Omics features are ranked using a voting-theory-inspired aggregation mechanism across models and subsamples, while the optimal number of features is selected via a Pareto front, balancing predictive accuracy and model sparsity without any user-defined thresholds. When applied to multi-omics datasets from three pancreatic cancer cohorts, hEFS identifies significantly fewer and more stable biomarkers compared to conventional, late-fusion CoxLasso models, while maintaining comparable discrimination performance. Implemented within the open-source mlr3fselect R package, hEFS offers a robust, interpretable, and clinically valuable tool for prognostic modelling and biomarker discovery in high-dimensional survival settings.

• Monopoli, Giulia; Sadeghinia, Javad; Sundnes, Joakim; Aabel, Eivind Westrum; Ribe, Margareth & Castrini, Anna Isotta [Show all 12 contributors for this article] (2025). Arrhythmic mitral valve syndrome insights from left ventricular shape analysis. Full text in Research Archive
• Monopoli, Giulia; Sadeghinia, Javad; Sundnes, Joakim; Aabel, Eivind Westrum; Ribe, Margareth & Castrini, Anna Isotta [Show all 12 contributors for this article] (2025). Arrhythmic mitral valve syndrome insights from left ventricular shape analysis. Full text in Research Archive
• Monopoli, Giulia; Wifstad, Sigurd Vangen; Kihldal, Henrik; Sadeghinia, Javad; Sundnes, Joakim & Aabel, Eivind Westrum [Show all 15 contributors for this article] (2025). MRI derived mitral valve and ventricle shape biomarkers for arrhythmic mitral valve syndrome. Full text in Research Archive
• Sundnes, Joakim (2025). Uncertainty quantification in cardiac mechanics. Full text in Research Archive
• Wojewodzic, Marcin Wlodzimierz & Rognes, Torbjørn (2025). AOP-networkFinder vs2. zenodo. doi: 10.5281/zenodo.15384273. Full text in Research Archive
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