Ingrid Kristine Glad

• Moen, Per August Jarval; Nielsen, Sebastian Grau; Urheim, Espen Bjørge; Tveten, Martin & Glad, Ingrid Kristine (2026). gridcp: Fast Online Changepoint Detection in Python. arXiv. doi: 10.48550/ARXIV.2608.18695. Full text in Research Archive
• Brandsæter, Andreas & Glad, Ingrid Kristine (2025). XAI in hindsight: Shapley values for explaining prediction accuracy. Expert Systems With Applications. ISSN 0957-4174. 273, p. 1–13. doi: 10.1016/j.eswa.2025.126845. Full text in Research Archive
• Salucci, Clara Bertinelli; Bakdi, Azzeddine; Glad, Ingrid Kristine; Lindqvist, Bo Henry; Vanem, Erik & Bin, Riccardo De (2025). Lifetime analysis with monotonic degradation: a boosted first hitting time model based on a homogeneous gamma process. Lifetime Data Analysis. ISSN 1380-7870. 31, p. 300–339. doi: 10.1007/s10985-025-09648-z. Full text in Research Archive
• Grindheim, Christian Alm; Stakkeland, Morten; Glad, Ingrid Kristine & Vanem, Erik (2025). Comparative Study of Machine Learning Methods for State of Health Estimation of Maritime Battery Systems. Journal of Offshore Mechanics and Arctic Engineering. ISSN 0892-7219. 147(3), p. 1–15. doi: 10.1115/1.4065967. Full text in Research Archive Show summary

  This paper tests two data-driven approaches for predicting the state of health (SOH) of lithium-ion-batteries (LIBs) for the purpose of monitoring maritime battery systems. First, non-sequential approaches are investigated and various models are tested: ridge, lasso, support vector regression, and gradient boosted trees. Binning is proposed for feature engineering for these types of models to capture the temporal structure in the data. Such binning creates histograms for the accumulated time the LIB has been within various voltage, temperature, and current ranges. Further binning to combine these histograms into 2D or 3D histograms is explored in order to capture relationships between voltage, temperature, and current. Second, a sequential approach is explored where different deep learning architectures are tried out: long short-term memory, transformer, and temporal convolutional network. Finally, the various models and the two approaches are compared in terms of their SOH prediction ability. Results indicate that the binning with ridge regression models performed best. The same publicly available sensor data from laboratory cycling tests are used for both approaches. Copyright © 2024 by ASME

• Olsen, Lars Henry Berge; Glad, Ingrid Kristine; Jullum, Martin & Aas, Kjersti (2024). A comparative study of methods for estimating model-agnostic Shapley value explanations. Data mining and knowledge discovery. ISSN 1384-5810. 38, p. 1782–1829. doi: 10.1007/s10618-024-01016-z. Full text in Research Archive Show summary

  Shapley values originated in cooperative game theory but are extensively used today as a model-agnostic explanation framework to explain predictions made by complex machine learning models in the industry and academia. There are several algorithmic approaches for computing different versions of Shapley value explanations. Here, we consider Shapley values incorporating feature dependencies, referred to as conditional Shapley values, for predictive models fitted to tabular data. Estimating precise conditional Shapley values is difficult as they require the estimation of non-trivial conditional expectations. In this article, we develop new methods, extend earlier proposed approaches, and systematize the new refined and existing methods into different method classes for comparison and evaluation. The method classes use either Monte Carlo integration or regression to model the conditional expectations. We conduct extensive simulation studies to evaluate how precisely the different method classes estimate the conditional expectations, and thereby the conditional Shapley values, for different setups. We also apply the methods to several real-world data experiments and provide recommendations for when to use the different method classes and approaches. Roughly speaking, we recommend using parametric methods when we can specify the data distribution almost correctly, as they generally produce the most accurate Shapley value explanations. When the distribution is unknown, both generative methods and regression models with a similar form as the underlying predictive model are good and stable options. Regression-based methods are often slow to train but quickly produce the Shapley value explanations once trained. The vice versa is true for Monte Carlo-based methods, making the different methods appropriate in different practical situations.

• Moen, Per August Jarval; Glad, Ingrid Kristine & Tveten, Martin (2024). Efficient sparsity adaptive changepoint estimation. Electronic Journal of Statistics. 18(2), p. 3975–4038. doi: 10.1214/24-EJS2294. Full text in Research Archive Show summary

  We propose a computationally efficient and sparsity adaptive procedure for estimating changes in unknown subsets of a high-dimensional data sequence. Assuming the data sequence is Gaussian, we prove that the new method successfully estimates the number and locations of changepoints with a given error rate and under minimal conditions for all sparsities of the changing subset. Our method has computational complexity linear up to logarithmic factors in both the length and number of time series, making it applicable to large data sets. Through extensive numerical studies we show that the new methodology is highly competitive in terms of both estimation accuracy and computational cost. The practical usefulness of the method is illustrated by analysing sensor data from a hydro power plant, and an efficient R implementation is available.

• Salucci, Clara Bertinelli; Bakdi, Azzeddine; Glad, Ingrid Kristine; Vanem, Erik & Bin, Riccardo De (2023). A novel semi-supervised learning approach for State of Health monitoring of maritime lithium-ion batteries. Journal of Power Sources. ISSN 0378-7753. 556. doi: 10.1016/j.jpowsour.2022.232429. Full text in Research Archive Show summary

  Lithium-ion batteries are a prominent technology for the electrification of the transport sector, which itself is a key measure towards the departure from fossil fuels. The “green shift” is taking place in the marine industry too, where the number of battery-powered vessels is fastly growing. In this case, monitoring the battery State of Health is essential more than ever to optimise battery use, promote safety, and ensure the coverage of ship power and energy demands. Classification societies typically require annual capacity tests for this purpose; however, the tests are disruptive, costly and time-consuming. As a consequence they are seldom, in addition to not being always fully reliable. We propose a novel alternative semi-supervised learning approach to estimate the State of Health of a lithium-ion battery system with no labelled data, starting from a minimal set of weakly labelled data from another similar system. The method is based on operational sensor data gathered from the battery, together with the battery State of Charge. Our results show that the procedure is valid, and the obtained estimates can be used to significantly progress in failure prevention, operational optimisation, and for planning batteries at the design stage.

• Bakka, Haakon Christopher; Rognebakke, Hanne Therese Wist; Glad, Ingrid Kristine; Haff, Ingrid Hobæk & Vanem, Erik (2022). Estimating the effect of biofouling on ship shaft power based on sensor measurements. Ship Technology Research. ISSN 0937-7255. 70(3), p. 209–221. doi: 10.1080/09377255.2022.2159108. Full text in Research Archive Show summary

  Marine biofouling on a ship's hull and propeller increases the resistance of the ship moving through water and reduces the propulsion efficiency of the ship. Estimating the effect of fouling is difficult, as the biomass is rarely measured. In this paper, we present a new data-driven model for the total shaft power use of a large containership, in order to estimate the unobserved effect of fouling. Due to the limitations of both physical models and machine learning models, we develop a Bayesian generalized additive model for our purpose. We discuss issues of representative training data for the model. Further, we subset and subsample the data to a representative sample. Models are compared by out-of-sample predictive quality, physical appropriateness, and through autocorrelation of residuals. The Bayesian generalized additive model combined with computational inference using integrated nested Laplace approximations gives a robust estimate of the biofouling effect over time. It also allows a decomposition of the total shaft power use into effects of speed, weather, and other conditions. This model can be used to understand the effectiveness and timing of different hull and propeller treatments.

• Salucci, Clara Bertinelli; Bakdi, Azzeddine; Glad, Ingrid Kristine; Vanem, Erik & Bin, Riccardo De (2022). A novel semi-supervised learning approach for maritime lithium-ion battery monitoring. In Torelli, Nicola; BELLIO, RUGGERO & MUGGEO, VITO (Ed.), Proceedings of the 36th International Workshop on Statistical Modelling. EUT Edizioni Università di Trieste. ISSN 9788855113090. p. 85–90. Full text in Research Archive Show summary

  We propose a novel semi-supervised learning method to monitor the State of Health of lithium-ion batteries, a prominent technology for the electrification of the transport sector. Our approach enables State of Health monitoring of batteries with no labeled data, starting from a minimal set of labeled data from another similar battery. This can be achieved by exploiting the relation between a pseudo-capacity measure and the total capacity of the labeled data. Our results with operational data from maritime batteries show that the approach is valid and can lead to significant progress in failure prevention, operational optimization, and for planning batteries at the design stage.

• Salucci, Clara Bertinelli; Bakdi, Azzeddine; Glad, Ingrid Kristine; Vanem, Erik & Bin, Riccardo De (2022). Multivariable Fractional Polynomials for lithium-ion batteries degradation models under dynamic conditions. Journal of Energy Storage. ISSN 2352-152X. 52. doi: 10.1016/j.est.2022.104903. Full text in Research Archive Show summary

  Longevity and safety of lithium-ion batteries are facilitated by efficient monitoring and adjustment of the battery operating conditions. Hence, it is crucial to implement fast and accurate algorithms for State of Health (SoH) monitoring on the Battery Management System. The task is challenging due to the complexity and multitude of the factors contributing to the battery capacity degradation, especially because the different degradation processes occur at various timescales and their interactions play an important role. Data-driven methods bypass this issue by approximating the complex processes with statistical or machine learning models: they rely solely on the available cycling data, while remaining agnostic to the underlying real physical processes. This paper proposes a data-driven approach which is understudied in the context of battery degradation, despite being characterised by simplicity and ease of computation: the Multivariable Fractional Polynomial (MFP) regression. Models are trained from historical data of one exhausted cell and used to predict the SoH of other cells. The data are provided by the NASA Ames Prognostics Center of Excellence, and are characterised by varying loads which simulate dynamic operating conditions. Two hypothetical scenarios are considered: one assumes that a recent observed capacity measurement is known, the other is based only on the nominal capacity of the cell. It was shown that the degradation behaviour of the batteries under examination is influenced by their historical data, as supported by the low prediction errors achieved (root mean squared errors ranging from 1.2% to 7.22% when considering data up to the battery End of Life). Moreover, we offer a multi-factor perspective where the degree of impact of each different factor to ageing acceleration is analysed. Finally, we compare with a Long Short-Term Memory Neural Network and other works from the literature on the same dataset. We conclude that the MFP regression is effective and competitive with contemporary works, and provides several additional advantages e.g. in terms of interpretability, generalisability, and implementability.

• Brandsæter, Andreas & Glad, Ingrid Kristine (2022). Shapley values for cluster importance: How clusters of the training data affect a prediction. Data mining and knowledge discovery. ISSN 1384-5810. doi: 10.1007/s10618-022-00896-3. Full text in Research Archive Show summary

  This paper proposes a novel approach to explain the predictions made by data-driven methods. Since such predictions rely heavily on the data used for training, explanations that convey information about how the training data affects the predictions are useful. The paper proposes a novel approach to quantify how different data-clusters of the training data affect a prediction. The quantification is based on Shapley values, a concept which originates from coalitional game theory, developed to fairly distribute the payout among a set of cooperating players. A player’s Shapley value is a measure of that player’s contribution. Shapley values are often used to quantify feature importance, ie. how features affect a prediction. This paper extends this to cluster importance, letting clusters of the training data act as players in a game where the predictions are the payouts. The novel methodology proposed in this paper lets us explore and investigate how different clusters of the training data affect the predictions made by any black-box model, allowing new aspects of the reasoning and inner workings of a prediction model to be conveyed to the users. The methodology is fundamentally different from existing explanation methods, providing insight which would not be available otherwise, and should complement existing explanation methods, including explanations based on feature importance.

• Olsen, Lars Henry Berge; Glad, Ingrid Kristine; Jullum, Martin & Aas, Kjersti (2022). Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features. Journal of machine learning research. ISSN 1532-4435. 23(213), p. 1–51. Full text in Research Archive Show summary

  Shapley values are today extensively used as a model-agnostic explanation framework to explain complex predictive machine learning models. Shapley values have desirable theoretical properties and a sound mathematical foundation in the field of cooperative game theory. Precise Shapley value estimates for dependent data rely on accurate modeling of the dependencies between all feature combinations. In this paper, we use a variational autoencoder with arbitrary conditioning (VAEAC) to model all feature dependencies simultaneously. We demonstrate through comprehensive simulation studies that our VAEAC approach to Shapley value estimation outperforms the state-of-the-art methods for a wide range of settings for both continuous and mixed dependent features. For high-dimensional settings, our VAEAC approach with a non-uniform masking scheme significantly outperforms competing methods. Finally, we apply our VAEAC approach to estimate Shapley value explanations for the Abalone data set from the UCI Machine Learning Repository.

• Lingjærde, Camilla; Lien, Tonje Gulbrandsen; Borgan, Ørnulf; Bergholtz, Helga & Glad, Ingrid K. (2021). Tailored graphical lasso for data integration in gene network reconstruction. BMC Bioinformatics. 22(1). doi: 10.1186/s12859-021-04413-z. Full text in Research Archive Show summary

  Background Identifying gene interactions is a topic of great importance in genomics, and approaches based on network models provide a powerful tool for studying these. Assuming a Gaussian graphical model, a gene association network may be estimated from multiomic data based on the non-zero entries of the inverse covariance matrix. Inferring such biological networks is challenging because of the high dimensionality of the problem, making traditional estimators unsuitable. The graphical lasso is constructed for the estimation of sparse inverse covariance matrices in such situations, using L1-penalization on the matrix entries. The weighted graphical lasso is an extension in which prior biological information from other sources is integrated into the model. There are however issues with this approach, as it naïvely forces the prior information into the network estimation, even if it is misleading or does not agree with the data at hand. Further, if an associated network based on other data is used as the prior, the method often fails to utilize the information effectively. Results We propose a novel graphical lasso approach, the tailored graphical lasso, that aims to handle prior information of unknown accuracy more effectively. We provide an R package implementing the method, tailoredGlasso. Applying the method to both simulated and real multiomic data sets, we find that it outperforms the unweighted and weighted graphical lasso in terms of all performance measures we consider. In fact, the graphical lasso and weighted graphical lasso can be considered special cases of the tailored graphical lasso, and a parameter determined by the data measures the usefulness of the prior information. We also find that among a larger set of methods, the tailored graphical is the most suitable for network inference from high-dimensional data with prior information of unknown accuracy. With our method, mRNA data are demonstrated to provide highly useful prior information for protein–protein interaction networks. Conclusions The method we introduce utilizes useful prior information more effectively without involving any risk of loss of accuracy should the prior information be misleading.

• Bakdi, Azzeddine; Glad, Ingrid Kristine & Vanem, Erik (2021). Testbed Scenario Design Exploiting Traffic Big Data for Autonomous Ship Trials Under Multiple Conflicts With Collision/Grounding Risks and Spatio-Temporal Dependencies. IEEE Transactions on Intelligent Transportation Systems (T-ITS). ISSN 1524-9050. 22(12). doi: 10.1109/TITS.2021.3095547. Full text in Research Archive Show summary

  Autonomous ships are promoted as the future of the maritime transport industry aiming to overcome conventional vessels in terms of performance, safety and environmental impact. Yet their tangled cyber-physical-social interactions and new emerging properties induce questions regarding their liability and trustworthiness. Digital simulations and sea trials are launched to assure the safety requirements and social expectations are met a priori. This paper presents the design of realistic testbed scenarios from huge historical data through a high-performance computational method to recommend a complete set of navigation scenarios for autonomy tests. The developed approach integrates traffic big data from Automatic Identification System (AIS) with high-resolution digital maps, vessel information registry, and digital nautical charts. All historical vessel-to-ground and vessel-to-vessel interactions are efficiently analyzed through a hierarchical method for collision and grounding conflicts assessment with a 15-minutes prediction horizon. Relative risk is evaluated accurately over full periods of predicted close-quarters situations subject to physical limits and sea-room availability for evasive maneuverers under COLREG rules and traffic separation schemes. Spatial dependencies among multiple conflicts define risky momentary traffic situations modelled through directed graph representation of nested interactions. Their temporal dependencies describe navigation scenarios through dynamic co-behaviors between multiple participating vessels over a period of time. Finally, we analyze negative/positive actions that increase/decrease the complexity. The presented algorithms are computationally very efficient, they scale to several (country*year)s where millions of scenarios are extracted, classified, and scored by their relative risk, complexity, and likelihood for firm post-test conclusions.

• Engebretsen, Solveig & Glad, Ingrid Kristine (2020). Partially linear monotone methods with automatic variable selection and monotonicity direction discovery. Statistics in Medicine. ISSN 0277-6715. 39(25), p. 3549–3568. doi: 10.1002/sim.8680. Full text in Research Archive Show summary

  In many statistical regression and prediction problems, it is reasonable to assume monotone relationships between certain predictor variables and the outcome. Genomic effects on phenotypes are, for instance, often assumed to be monotone. However, in some settings, it may be reasonable to assume a partially linear model, where some of the covariates can be assumed to have a linear effect. One example is a prediction model using both high‐dimensional gene expression data, and low‐dimensional clinical data, or when combining continuous and categorical covariates. We study methods for fitting the partially linear monotone model, where some covariates are assumed to have a linear effect on the response, and some are assumed to have a monotone (potentially nonlinear) effect. Most existing methods in the literature for fitting such models are subject to the limitation that they have to be provided the monotonicity directions a priori for the different monotone effects. We here present methods for fitting partially linear monotone models which perform both automatic variable selection, and monotonicity direction discovery. The proposed methods perform comparably to, or better than, existing methods, in terms of estimation, prediction, and variable selection performance, in simulation experiments in both classical and high‐dimensional data settings.

• Bakdi, Azzeddine; Glad, Ingrid Kristine; Vanem, Erik & Engelhardtsen, Øystein (2020). AIS-Based Multiple Vessel Collision and Grounding Risk Identification based on Adaptive Safety Domain. Journal of Marine Science and Engineering (JMSE). 8(1). doi: 10.3390/jmse8010005. Full text in Research Archive Show summary

  The continuous growth in maritime traffic and recent developments towards autonomous navigation have directed increasing attention to navigational safety in which new tools are required to identify real-time risk and complex navigation situations. These tools are of paramount importance to avoid potentially disastrous consequences of accidents and promote safe navigation at sea. In this study, an adaptive ship-safety-domain is proposed with spatial risk functions to identify both collision and grounding risk based on motion and maneuverability conditions for all vessels. The algorithm is designed and validated through extensive amounts of Automatic Identification System (AIS) data for decision support over a large area, while the integration of the algorithm with other navigational systems will increase effectiveness and ensure reliability. Since a successful evacuation of a potential vessel-to-vessel collision, or a vessel grounding situation, is highly dependent on the nearby maneuvering limitations and other possible accident situations, multi-vessel collision and grounding risk is considered in this work to identify real-time risk. The presented algorithm utilizes and exploits dynamic AIS information, vessel registry and high-resolution maps and it is robust to inaccuracies of position, course and speed over ground records. The computation-efficient algorithm allows for real-time situation risk identification at a large-scale monitored map up to country level and up to several years of operation with a very high accuracy.

• Salvatore, Stefania; Rand, Knut Dagestad; Grytten, Ivar; Ferkingstad, Egil; Domanska, Diana & Holden, Lars [Show all 10 contributors for this article] (2019). Beware the Jaccard: the choice of similarity measure is important and non-trivial in genomic colocalisation analysis. Briefings in Bioinformatics. ISSN 1467-5463. p. 1–8. doi: 10.1093/bib/bbz083. Full text in Research Archive
• Brandsæter, Andreas; Vanem, Erik & Glad, Ingrid Kristine (2019). Efficient on-line anomaly detection for ship systems in operation. Expert Systems With Applications. ISSN 0957-4174. 121, p. 418–437. doi: 10.1016/j.eswa.2018.12.040. Full text in Research Archive Show summary

  We propose novel modifications to an anomaly detection methodology based on multivariate signal reconstruction followed by residuals analysis. The reconstructions are made using Auto Associative Kernel Regression (AAKR), where the query observations are compared to historical observations called memory vectors, representing normal operation. When the data set with historical observations grows large, the naive approach where all observations are used as memory vectors will lead to unacceptable large computational loads, hence a reduced set of memory vectors should be intelligently selected. The residuals between the observed and the reconstructed signals are analysed using standard Sequential Probability Ratio Tests (SPRT), where appropriate alarms are raised based on the sequential behaviour of the residuals. The modifications we introduce include: a novel cluster based method to select memory vectors to be considered by the AAKR, which gives an extensive reduction in computation time; a generalization of the distance measure, which makes it possible to distinguish between explanatory and response variables; and a regional credibility estimation used in the residuals analysis, to let the time used to identify if a sequence of query vectors represents an anomalous state or not, depend on the amount of data situated close to or surrounding the query vector. We demonstrate how the anomaly detection method and the proposed modifications can be successfully applied for anomaly detection on a set of imbalanced benchmark data sets, as well as on recent data from a marine diesel engine in operation.

• Engebretsen, Solveig & Glad, Ingrid Kristine (2019). Additive monotone regression in high and lower dimensions. Statistics Surveys. 13, p. 1–51. doi: 10.1214/19-SS124. Full text in Research Archive
• Grytten, Ivar; Rand, Knut Dagestad; Nederbragt, Alexander Johan; Storvik, Geir Olve; Glad, Ingrid Kristine & Sandve, Geir Kjetil (2019). Graph Peak Caller: Calling ChIP-seq peaks on graph-based reference genomes. PLoS Computational Biology. ISSN 1553-734X. 15(2), p. 1–13. doi: 10.1371/journal.pcbi.1006731. Full text in Research Archive Show summary

  Graph-based representations are considered to be the future for reference genomes, as they allow integrated representation of the steadily increasing data on individual variation. Currently available tools allow de novo assembly of graph-based reference genomes, alignment of new read sets to the graph representation as well as certain analyses like variant calling and haplotyping. We here present a first method for calling ChIP-Seq peaks on read data aligned to a graph-based reference genome. The method is a graph generalization of the peak caller MACS2, and is implemented in an open source tool, Graph Peak Caller. By using the existing tool vg to build a pan-genome of Arabidopsis thaliana, we validate our approach by showing that Graph Peak Caller with a pan-genome reference graph can trace variants within peaks that are not part of the linear reference genome, and find peaks that in general are more motif-enriched than those found by MACS2.

• Lien, Tonje Gulbrandsen; Borgan, Ørnulf; Reppe, Sjur; Gautvik, Kaare M & Glad, Ingrid Kristine (2018). Integrated analysis of DNA-methylation and gene expression using high-dimensional penalized regression: a cohort study on bone mineral density in postmenopausal women. BMC Medical Genomics. 11(24). doi: 10.1186/s12920-018-0341-2. Full text in Research Archive Show summary

  Background Using high-dimensional penalized regression we studied genome-wide DNA-methylation in bone biopsies of 80 postmenopausal women in relation to their bone mineral density (BMD). The women showed BMD varying from severely osteoporotic to normal. Global gene expression data from the same individuals was available, and since DNA-methylation often affects gene expression, the overall aim of this paper was to include both of these omics data sets into an integrated analysis. Methods The classical penalized regression uses one penalty, but we incorporated individual penalties for each of the DNA-methylation sites. These individual penalties were guided by the strength of association between DNA-methylations and gene transcript levels. DNA-methylations that were highly associated to one or more transcripts got lower penalties and were therefore favored compared to DNA-methylations showing less association to expression. Because of the complex pathways and interactions among genes, we investigated both the association between DNA-methylations and their corresponding cis gene, as well as the association between DNA-methylations and trans-located genes. Two integrating penalized methods were used: first, an adaptive group-regularized ridge regression, and secondly, variable selection was performed through a modified version of the weighted lasso. Results When information from gene expressions was integrated, predictive performance was considerably improved, in terms of predictive mean square error, compared to classical penalized regression without data integration. We found a 14.7% improvement in the ridge regression case and a 17% improvement for the lasso case. Our version of the weighted lasso with data integration found a list of 22 interesting methylation sites. Several corresponded to genes that are known to be important in bone formation. Using BMD as response and these 22 methylation sites as covariates, least square regression analyses resulted in R2=0.726, comparable to an average R2=0.438 for 10000 randomly selected groups of DNA-methylations with group size 22. Conclusions Two recent types of penalized regression methods were adapted to integrate DNA-methylation and their association to gene expression in the analysis of bone mineral density. In both cases predictions clearly benefit from including the additional information on gene expressions.

• Brandsæter, Andreas; Vanem, Erik & Glad, Ingrid Kristine (2017). Cluster Based Anomaly Detection with Applications in the Maritime Industry. In Li, Chuan; Oliveira, José Valente de; Ding, Ping; Guo, Wei; Shi, Juanjuan & Bai, Yun (Ed.), Proceedings of the 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control SDPC 2017. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781509040209. p. 328–333. doi: 10.1109/SDPC.2017.69. Full text in Research Archive
• Simovski, Boris; Vodak, Daniel; Gundersen, Sveinung; Domanska, Diana Ewa; Azab, Abdulrahman & Holden, Lars [Show all 25 contributors for this article] (2017). GSuite HyperBrowser: integrative analysis of dataset collections across the genome and epigenome. GigaScience. 6(7), p. 1–12. doi: 10.1093/gigascience/gix032. Full text in Research Archive Show summary

  Background: Recent large-scale undertakings such as ENCODE and Roadmap Epigenomics have generated experimental data mapped to the human reference genome (as genomic tracks) representing a variety of functional elements across a large number of cell types. Despite the high potential value of these publicly available data for a broad variety of investigations, little attention has been given to the analytical methodology necessary for their widespread utilisation. Findings: We here present a first principled treatment of the analysis of collections of genomic tracks. We have developed novel computational and statistical methodology to permit comparative and confirmatory analyses across multiple and disparate data sources. We delineate a set of generic questions that are useful across a broad range of investigations and discuss the implications of choosing different statistical measures and null models. Examples include contrasting analyses across different tissues or diseases. The methodology has been implemented in a comprehensive open-source software system, the GSuite HyperBrowser. To make the functionality accessible to biologists, and to facilitate reproducible analysis, we have also developed a web-based interface providing an expertly guided and customizable way of utilizing the methodology. With this system, many novel biological questions can flexibly be posed and rapidly answered. Conclusions: Through a combination of streamlined data acquisition, interoperable representation of dataset collections and customizable statistical analysis with guided setup and interpretation, the GSuite HyperBrowser represents a first comprehensive solution for integrative analysis of track collections across the genome and epigenome. The software is available at: https://hyperbrowser.uio.no

• Rand, Knut Dagestad; Grytten, Ivar; Nederbragt, Alexander Johan; Storvik, Geir Olve; Glad, Ingrid Kristine & Sandve, Geir Kjetil (2017). Coordinates and intervals in graph-based reference genomes. BMC Bioinformatics. 18:263, p. 1–8. doi: 10.1186/s12859-017-1678-9. Full text in Research Archive Show summary

  Background It has been proposed that future reference genomes should be graph structures in order to better represent the sequence diversity present in a species. However, there is currently no standard method to represent genomic intervals, such as the positions of genes or transcription factor binding sites, on graph-based reference genomes. Results We formalize offset-based coordinate systems on graph-based reference genomes and introduce methods for representing intervals on these reference structures. We show the advantage of our methods by representing genes on a graph-based representation of the newest assembly of the human genome (GRCh38) and its alternative loci for regions that are highly variable. Conclusion More complex reference genomes, containing alternative loci, require methods to represent genomic data on these structures. Our proposed notation for genomic intervals makes it possible to fully utilize the alternative loci of the GRCh38 assembly and potential future graph-based reference genomes. We have made a Python package for representing such intervals on offset-based coordinate systems, available at https://github.com/uio-cels/offsetbasedgraph . An interactive web-tool using this Python package to visualize genes on a graph created from GRCh38 is available at https://github.com/uio-cels/genomicgraphcoords .

• Reppe, Sjur; Lien, Tonje Gulbrandsen; Hsu, Yi-Hsiang; Gautvik, Vigdis Teig; Olstad, Ole Kristoffer & Yu, Rona [Show all 11 contributors for this article] (2017). Distinct DNA methylation profiles in bone and blood of osteoporotic and healthy postmenopausal women. Epigenetics. ISSN 1559-2294. 12(8), p. 674–687. doi: 10.1080/15592294.2017.1345832. Full text in Research Archive Show summary

  DNA methylation affects expression of associated genes and may contribute to the missing genetic effects from genome-wide association studies of osteoporosis. To improve insight into the mechanisms of postmenopausal osteoporosis, we combined transcript profiling with DNA methylation analyses in bone. RNA and DNA were isolated from 84 bone biopsies of postmenopausal donors varying markedly in bone mineral density (BMD). In all, 2529 CpGs in the top 100 genes most significantly associated with BMD were analyzed. The methylation levels at 63 CpGs differed significantly between healthy and osteoporotic women at 10% false discovery rate (FDR). Five of these CpGs at 5% FDR could explain 14% of BMD variation. To test whether blood DNA methylation reflect the situation in bone (as shown for other tissues), an independent cohort was selected and BMD association was demonstrated in blood for 13 of the 63 CpGs. Four transcripts representing inhibitors of bone metabolism—MEPE, SOST, WIF1, and DKK1—showed correlation to a high number of methylated CpGs, at 5% FDR. Our results link DNA methylation to the genetic influence modifying the skeleton, and the data suggest a complex interaction between CpG methylation and gene regulation. This is the first study in the hitherto largest number of postmenopausal women to demonstrate a strong association among bone CpG methylation, transcript levels, and BMD/fracture. This new insight may have implications for evaluation of osteoporosis stage and susceptibility. The final version of this research has been published in Epigenetics. © 2017 Taylor & Francis

• Frigessi, Arnoldo; Buhlmann, Peter; Glad, Ingrid Kristine; Richardson, Sylvia & Vannucci, Marina (2016). Some themes in high-dimensional statistics. Abel Symposia. ISSN 2193-2808. 11, p. 1–13. doi: 10.1007/978-3-319-27099-9_1. Full text in Research Archive
• Solbergersen, LInn Cecilie; Ahmed, Ismail; Frigessi, Arnoldo; Glad, Ingrid Kristine & Richardson, Sylvia Therese Lamblin (2016). Preselection in Lasso-Type Analysis for Ultra-High Dimensional Genomic Exploration. Abel Symposia. ISSN 2193-2808. 11, p. 37–66. doi: 10.1007/978-3-319-27099-9_3. Full text in Research Archive
• Brandsæter, Andreas; Manno, Gabriele; Vanem, Erik & Glad, Ingrid Kristine (2016). An Application of Sensor-Based Anomaly Detection in the Maritime Industry. In NA, NA (Eds.), 2016 IEEE International Conference on Prognostics and Health Management (ICPHM 2016). Curran Associates, Inc.. ISSN 9781509003839. doi: 10.1109/icphm.2016.7811910. Full text in Research Archive
• Glad, Ingrid Kristine & Hjort, Nils Lid (2016). Model uncertainty first, not afterwards. Statistical Science. ISSN 0883-4237. 31(4), p. 490–494. doi: 10.1214/16-STS559. Full text in Research Archive Show summary

  Watson and Holmes propose ways of investigating robustness of statistical decisions by examining certain neighbourhoods around a posterior distribution. This may partly amount to ad hoc modelling of extra uncertainty. Instead of creating neighbourhoods around the posterior a posteriori, we argue that it might be more fruitful to model a layer of extra uncertainty first, in the model building process, and then allow the data to determine how big the resulting neighbourhoods ought to be. We develop and briefly illustrate a general strategy along such lines.

• Bergersen, Linn Cecilie; Tharmaratnam, Kukatharmini & Glad, Ingrid Kristine (2014). Monotone Splines Lasso. Computational Statistics & Data Analysis. ISSN 0167-9473. 77, p. 336–351. doi: 10.1016/j.csda.2014.03.013. Full text in Research Archive
• Antoniadis, Anestis; Glad, Ingrid Kristine & Mohammed, Hayat (2014). Local comparison of empirical distributions via nonparametric regression. Journal of Statistical Computation and Simulation. ISSN 0094-9655. 85(12), p. 2384–2405. doi: 10.1080/00949655.2014.929133. Full text in Research Archive
• Sandve, Geir Kjetil; Gundersen, Sveinung; Johansen, Morten; Glad, Ingrid Kristine; Gunathasan, Krishanthi & Holden, Lars [Show all 21 contributors for this article] (2013). The Genomic HyperBrowser: an analysis web server for genome-scale data. Nucleic Acids Research (NAR). ISSN 0305-1048. 41(W1), p. W133–W141. doi: 10.1093/nar/gkt342. Full text in Research Archive Show summary

  The immense increase in availability of genomic scale datasets, such as those provided by the ENCODE and Roadmap Epigenomics projects, presents unprecedented opportunities for individual researchers to pose novel falsifiable biological questions. With this opportunity, however, researchers are faced with the challenge of how to best analyze and interpret their genome-scale datasets. A powerful way of representing genome-scale data is as feature-specific coordinates relative to reference genome assemblies, i.e. as genomic tracks. The Genomic HyperBrowser (http://hyperbrowser.uio.no) is an open-ended web server for the analysis of genomic track data. Through the provision of several highly customizable components for processing and statistical analysis of genomic tracks, the HyperBrowser opens for a range of genomic investigations, related to, e.g., gene regulation, disease association or epigenetic modifications of the genome.

• Paulsen, Jonas; Lien, Tonje Gulbrandsen; Sandve, Geir Kjetil; Holden, Lars; Borgan, Ørnulf & Glad, Ingrid Kristine [Show all 7 contributors for this article] (2013). Handling realistic assumptions in hypothesis testing of 3D co-localization of genomic elements. Nucleic Acids Research (NAR). ISSN 0305-1048. 41(10), p. 5164–5174. doi: 10.1093/nar/gkt227. Full text in Research Archive Show summary

  The study of chromatin 3D structure has recently gained much focus owing to novel techniques for detecting genome-wide chromatin contacts using next-generation sequencing. A deeper understanding of the architecture of the DNA inside the nucleus is crucial for gaining insight into fundamental processes such as transcriptional regulation, genome dynamics and genome stability. Chromatin conformation capture-based methods, such as Hi-C and ChIA-PET, are now paving the way for routine genome-wide studies of chromatin 3D structure in a range of organisms and tissues. However, appropriate methods for analyzing such data are lacking. Here, we propose a hypothesis test and an enrichment score of 3D co-localization of genomic elements that handles intra- or interchromosomal interactions, both separately and jointly, and that adjusts for biases caused by structural dependencies in the 3D data. We show that maintaining structural properties during resampling is essential to obtain valid estimation of P-values. We apply the method on chromatin states and a set of mutated regions in leukemia cells, and find significant co-localization of these elements, with varying enrichment scores, supporting the role of chromatin 3D structure in shaping the landscape of somatic mutations in cancer.

• Serio, C di; Pellin, Danilo; ambrosi, alessandro; Glad, Ingrid Kristine & Frigessi, Arnoldo (2012). Biostatistics Meets Bioinformatics in Integrating Information from Highdimensional Heterogeneous Genomic Data: Two Examples from Rare Genetic Diseases and Infectious Diseases. In Biganzoli, E; Vellido, A; Ambrogi, F & Tagliaferri, R (Ed.), Computational Intelligence methods for bioinformatics and biostatistics. Springer. ISSN 9783642356865. p. 10–20. doi: 10.1007/978-3-642-35686-5_2. Full text in Research Archive
• Ambrosi, Alessandro; Glad, Ingrid Kristine; Pellin, Danilo; Cattoglio, Claudia; Mavilio, Fulvio & serio, clelia di [Show all 7 contributors for this article] (2011). Estimated Comparative Integration Hotspots Identify Different Behaviors of Retroviral Gene Transfer Vectors. PLoS Computational Biology. ISSN 1553-734X. 7(12). doi: 10.1371/journal.pcbi.1002292. Full text in Research Archive
• Bergersen, Linn Cecilie; Glad, Ingrid Kristine & Lyng, Heidi (2011). Weighted Lasso with Data Integration. Statistical Applications in Genetics and Molecular Biology. 10(1). doi: 10.2202/1544-6115.1703. Full text in Research Archive
• Sandve, Geir Kjetil; Gundersen, Sveinung; Rydbeck, Halfdan; Glad, Ingrid Kristine; Holden, Lars & Holden, Marit [Show all 15 contributors for this article] (2011). The differential disease regulome. BMC Genomics. 12, p. 353–353. doi: 10.1186/1471-2164-12-353. Full text in Research Archive Show summary

  Background Transcription factors in disease-relevant pathways represent potential drug targets, by impacting a distinct set of pathways that may be modulated through gene regulation. The influence of transcription factors is typically studied on a per disease basis, and no current resources provide a global overview of the relations between transcription factors and disease. Furthermore, existing pipelines for related large-scale analysis are tailored for particular sources of input data, and there is a need for generic methodology for integrating complementary sources of genomic information. Results We here present a large-scale analysis of multiple diseases versus multiple transcription factors, with a global map of over-and under-representation of 446 transcription factors in 1010 diseases. This map, referred to as the differential disease regulome, provides a first global statistical overview of the complex interrelationships between diseases, genes and controlling elements. The map is visualized using the Google map engine, due to its very large size, and provides a range of detailed information in a dynamic presentation format. The analysis is achieved through a novel methodology that performs a pairwise, genome-wide comparison on the cartesian product of two distinct sets of annotation tracks, e.g. all combinations of one disease and one TF. The methodology was also used to extend with maps using alternative data sets related to transcription and disease, as well as data sets related to Gene Ontology classification and histone modifications. We provide a web-based interface that allows users to generate other custom maps, which could be based on precisely specified subsets of transcription factors and diseases, or, in general, on any categorical genome annotation tracks as they are improved or become available. Conclusion We have created a first resource that provides a global overview of the complex relations between transcription factors and disease. As the accuracy of the disease regulome depends mainly on the quality of the input data, forthcoming ChIP-seq based binding data for many TFs will provide improved maps. We further believe our approach to genome analysis could allow an advance from the current typical situation of one-time integrative efforts to reproducible and upgradable integrative analysis. The differential disease regulome and its associated methodology is available at http://hyperbrowser.uio.no

• Sandve, Geir Kjetil; Gundersen, Sveinung; Rydbeck, Halfdan; Glad, Ingrid Kristine; Holden, Lars & Holden, Marit [Show all 14 contributors for this article] (2010). The Genomic HyperBrowser: inferential genomics at the sequence level. Genome Biology. ISSN 1465-6906. 11(12). doi: 10.1186/gb-2010-11-12-r121. Full text in Research Archive Show summary

  The immense increase in the generation of genomic scale data poses an unmet analytical challenge, due to a lack of established methodology with the required flexibility and power. We propose a first principled approach to statistical analysis of sequence-level genomic information. We provide a growing collection of generic biological investigations that query pairwise relations between tracks, represented as mathematical objects, along the genome. The Genomic HyperBrowser implements the approach and is available at http://hyperbrowser.uio.no.

• Lando, M; Holden, Marit; Bergersen, LC; Glad, Ingrid K; Kristensen, GB & Lyng, H [Show all 9 contributors for this article] (2009). Gene Dosage, Expression, and Ontology Analysis Identifies Driver Genes in the Carcinogenesis and Chemoradioresistance of Cervical Cancer. PLoS Genetics. ISSN 1553-7390. 5(11). doi: 10.1371/journal.pgen.1000719. Full text in Research Archive Show summary

  Integrative analysis of gene dosage, expression, and ontology (GO) data was performed to discover driver genes in the carcinogenesis and chemoradioresistance of cervical cancers. Gene dosage and expression profiles of 102 locally advanced cervical cancers were generated by microarray techniques. Fifty-two of these patients were also analyzed with the Illumina expression method to confirm the gene expression results. An independent cohort of 41 patients was used for validation of gene expressions associated with clinical outcome. Statistical analysis identified 29 recurrent gains and losses and 3 losses (on 3p, 13q, 21q) associated with poor outcome after chemoradiotherapy. The intratumor heterogeneity, assessed from the gene dosage profiles, was low for these alterations, showing that they had emerged prior to many other alterations and probably were early events in carcinogenesis. Integration of the alterations with gene expression and GO data identified genes that were regulated by the alterations and revealed five biological processes that were significantly overrepresented among the affected genes: apoptosis, metabolism, macromolecule localization, translation, and transcription. Four genes on 3p (RYBP, GBE1) and 13q (FAM48A, MED4) correlated with outcome at both the gene dosage and expression level and were satisfactorily validated in the independent cohort. These integrated analyses yielded 57 candidate drivers of 24 genetic events, including novel loci responsible for chemoradioresistance. Further mapping of the connections among genetic events, drivers, and biological processes suggested that each individual event stimulates specific processes in carcinogenesis through the coordinated control of multiple genes. The present results may provide novel therapeutic opportunities of both early and advanced stage cervical cancers.

• Nygaard, V; Liu, Fang; Holden, Marit; Kuo, Winston P.; Trimarchi, Jeff & Ohno-Machado, Lucila [Show all 12 contributors for this article] (2008). Validation of oligoarrays for quantitative exploration of the transcriptome. BMC Genomics. 9. doi: 10.1186/1471-2164-9-258. Full text in Research Archive Show summary

  Background Oligoarrays have become an accessible technique for exploring the transcriptome, but it is presently unclear how absolute transcript data from this technique compare to the data achieved with tag-based quantitative techniques, such as massively parallel signature sequencing (MPSS) and serial analysis of gene expression (SAGE). By use of the TransCount method we calculated absolute transcript concentrations from spotted oligoarray intensities, enabling direct comparisons with tag counts obtained with MPSS and SAGE. The tag counts were converted to number of transcripts per cell by assuming that the sum of all transcripts in a single cell was 5·105. Our aim was to investigate whether the less resource demanding and more widespread oligoarray technique could provide data that were correlated to and had the same absolute scale as those obtained with MPSS and SAGE. Results A number of 1,777 unique transcripts were detected in common for the three technologies and served as the basis for our analyses. The correlations involving the oligoarray data were not weaker than, but, similar to the correlation between the MPSS and SAGE data, both when the entire concentration range was considered and at high concentrations. The data sets were more strongly correlated at high transcript concentrations than at low concentrations. On an absolute scale, the number of transcripts per cell and gene was generally higher based on oligoarrays than on MPSS and SAGE, and ranged from 1.6 to 9,705 for the 1,777 overlapping genes. The MPSS data were on same scale as the SAGE data, ranging from 0.5 to 3,180 (MPSS) and 9 to1,268 (SAGE) transcripts per cell and gene. The sum of all transcripts per cell for these genes was 3.8·105 (oligoarrays), 1.1·105 (MPSS) and 7.6·104 (SAGE), whereas the corresponding sum for all detected transcripts was 1.1·106 (oligoarrays), 2.8·105 (MPSS) and 3.8·105 (SAGE). Conclusion The oligoarrays and TransCount provide quantitative transcript concentrations that are correlated to MPSS and SAGE data, but, the absolute scale of the measurements differs across the technologies. The discrepancy questions whether the sum of all transcripts within a single cell might be higher than the number of 5·105 suggested in the literature and used to convert tag counts to transcripts per cell. If so, this may explain the apparent higher transcript detection efficiency of the oligoarrays, and has to be clarified before absolute transcript concentrations can be interchanged across the technologies. The ability to obtain transcript concentrations from oligoarrays opens up the possibility of efficient generation of universal transcript databases with low resource demands.

• Glad, Ingrid Kristine; Hjort, Nils Lid & Ushakov, N. G. (2007). Mean-squared error of kernel estimators for finite values of the sample size. Journal of Mathematical Sciences. ISSN 1072-3374. 146(4), p. 5977–5983. Full text in Research Archive
• Wiel, Mark van de; Holden, Marit; Glad, Ingrid Kristine; Lyng, Heidi & Frigessi, Arnoldo (2006). Bayesian process-based modeling of two-channel microarray experiments estimating absolute mRNA concentrations. In Do, Kim-Anh; Müller, Peter & Vannucci, M. (Ed.), Bayesian Inference for Gene Expression and Proteomics. Cambridge University Press. ISSN 9780521860925. p. 75–97. Full text in Research Archive
• Kristensen, Vessela Nedelcheva; Sorlie, Therese; Sørlie, Therese; Geisler, Jürgen; Geisler, E & Yoshimura, Noriko [Show all 14 contributors for this article] (2005). Effects of anastrozole on the intratumoral gene expression in locally advanced breast cancer. Journal of Steroid Biochemistry and Molecular Biology. ISSN 0960-0760. 95(1-5), p. 105–111. doi: 10.1016/j.jsbmb.2005.04.028. Full text in Research Archive
• Scheel, Ida; Aldrin, Magne; Glad, Ingrid Kristine; Sorum, Ragnhild; Lyng, Heidi & Frigessi, Arnoldo (2005). The influence of missing value imputation on detection of differentially expressed genes from microarray data. Bioinformatics. ISSN 1367-4803. 21(23), p. 4272–4279. Full text in Research Archive
• Frigessi, Arnoldo; Wiel, Mark van de; Holden, Marit; Svendsrud, Debbie H.; Glad, Ingrid Kristine & Lyng, Heidi (2005). Genome-wide estimation of transcript concentrations from spotted cDNA microarray data. Nucleic Acids Research (NAR). ISSN 0305-1048. 33(17). doi: 10.1093/nar/gni141. Full text in Research Archive Show summary

  A method providing absolute transcript concentrations from spotted microarray intensity data is presented. Number of transcripts per µg total RNA, mRNA or per cell, are obtained for each gene, enabling comparisons of transcript levels within and between tissues. The method is based on Bayesian statistical modelling incorporating available information about the experiment from target preparation to image analysis, leading to realistically large confidence intervals for estimated concentrations. The method was validated in experiments using transcripts at known concentrations, showing accuracy and reproducibility of estimated concentrations, which were also in excellent agreement with results from quantitative real-time PCR. We determined the concentration for 10 157 genes in cervix cancers and a pool of cancer cell lines and found values in the range of 105–1010 transcripts per µg total RNA. The precision of our estimates was sufficiently high to detect significant concentration differences between two tumours and between different genes within the same tumour, comparisons that are not possible with standard intensity ratios. Our method can be used to explore the regulation of pathways and to develop individualized therapies, based on absolute transcript concentrations. It can be applied broadly, facilitating the construction of the transcriptome, continuously updating it by integrating future data.

• Lingjærde, Ole Christian; Baumbusch, Lars Oliver; Liestøl, Knut; Glad, Ingrid Kristine & Børresen-Dale, Anne-Lise (2004). CGH-Explorer: a program for analysis of array-CGH data. Bioinformatics. ISSN 1367-4803. 21. doi: 10.1093/bioinformatics/bti113. Full text in Research Archive
• Glad, Ingrid Kristine; Hjort, Nils Lid & Ushakov, N.G. (2003). Correction of Density Estimators that are not Densities. Scandinavian Journal of Statistics. ISSN 0303-6898. 30(2), p. 415–427. Full text in Research Archive
• Chu, C. W.; Glad, Ingrid Kristine; Godtliebsen, F. & Marron, J.S. (1998). Edge preserving smoothers for image processing - Rejoinder. Journal of the American Statistical Association. ISSN 0162-1459. 93(442), p. 553–556. Full text in Research Archive
• Glad, Ingrid Kristine (1998). Bayesian projection of the acquired immune deficiency syndrome epidemic - Discussion. JRSS-C (Applied Statistics). 47(4). Full text in Research Archive
• Glad, Ingrid Kristine (1998). A note on unconditional properties of a parametrically guided Nadaraya-Watson estimator. Statistics and Probability Letters. ISSN 0167-7152. 37(1), p. 101–108. Full text in Research Archive
• Glad, Ingrid Kristine (1998). Parametrically guided nonparametric regression. Scandinavian Journal of Statistics. ISSN 0303-6898. 25(4), p. 649–668. Full text in Research Archive
• Glad, Ingrid Kristine & Sebastiani, Giovanni (1995). A Bayesian approach to synthetic magnetic resonance imaging. Biometrika. ISSN 0006-3444. 82(2), p. 237–250. Full text in Research Archive
• Hjort, Nils Lid & Glad, Ingrid Kristine (1995). Nonparametric density estimation with a parametric start. Annals of Statistics. ISSN 0090-5364. 23(3), p. 882–904. Full text in Research Archive
• Glad, Ingrid Kristine (1994). Bayesian Prediction of MR Images. In Dutter, R. & Grossmann, W. (Ed.), Short Communications in Computational Statistics. Physica Verlag, Heidelberg. p. 59–60. Full text in Research Archive
• Glad, Ingrid Kristine & Krogstad, Harald E. (1992). The Maximum-Likelihood Property of Estimators of Wave Parameters from Heave, Pitch, and Roll Buoys. Journal of Atmospheric and Oceanic Technology. ISSN 0739-0572. 9(2), p. 169–173. Full text in Research Archive

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• Frigessi, Arnoldo; Bühlmann, Peter; Glad, Ingrid Kristine; Langaas, Mette; Richardson, Sylvia Therese Lamblin & Vannucci, Marina (2016). Statistical Analysis for High-Dimensional Data. Springer. ISBN 9783319270975. 306 p. Full text in Research Archive

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• Eskeland, Gunnar; Glad, Ingrid Kristine & Frigessi, Arnoldo (2026). Europa må regulere KI | DN. Full text in Research Archive Show summary

  Høvdingene innen kunstig intelligens (KI) babler om trusler mot menneskeheten og ber om regulering, og da kreves skepsis i responsen (jf. Mogstad og Hardings kronikk i DN 2. oktober: «Hvorfor innovere når man kan regulere?»).Det de ber om, skal de ikke få.Ikke i Europa.

• 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
• Glad, Ingrid Kristine (2026). Intervju: Pisa-resultater 2025 i matte. [Internet]. Uniforum. Full text in Research Archive
• Glad, Ingrid Kristine (2026). On the Move: Real-Time Detection of Distributional Changes and Anomalies in Data Streams. Full text in Research Archive Show summary

  Online change and anomaly detection—the task of automatically detecting distributional changes in data streams in real time—is fundamental in applications such as sensor network monitoring, financial surveillance, cybersecurity, and the monitoring of AI processes and ML operations for anomalous behavior or drift. In this talk, I will present ongoing work on both recurrent-neural-network-based and more statistically grounded methods for fast, automatic, and scalable online detection of changes, to be used in settings where sequential information must be monitored in real time for deviations from some (unknown) baseline behavior. The methods are straightforward to apply and come with mathematical guarantees for optimal performance, for example high probability bounds on detection delays and error rates. I will conclude with a brief overview of the broader context of this project: the CoE Integreat – Norwegian Center for Knowledge-driven Machine Learning.

• Glad, Ingrid Kristine (2026). Women in AI: From Algorithms to Action: Living in the AI Era - NORA - Norwegian Artificial Intelligence Research Consortium. Full text in Research Archive
• Glad, Ingrid Kristine & Øksnes, Berit Omli (2026). Kvinner og KI - Rådgiverdagen 2026 (UiO). doi: https:/www.integreat.no/news/women-in-ai.html. Full text in Research Archive Show summary

  Rådgiverdagen ved Universitetet i Oslo (UiO) er en faglig samling for rådgivere og andre som jobber med studie- og karriereveiledning. Foredraget handlet om veier inn i matematikk og kunstig intelligens, betydningen av synlige forbilder, og hvorfor mangfold er viktig i forskningsfeltet.

• Glad, Ingrid Kristine (2025). KI-forskning ved MN-UiO-Integreat. Full text in Research Archive
• Glad, Ingrid Kristine (2025). "Integreat - Norwegian Centre for Knowledge-driven Machine Learning”. Full text in Research Archive
• Glad, Ingrid Kristine & Frigessi, Arnoldo (2025). KI = Statistikk2.0 ? Full text in Research Archive Show summary

  Foredrag i anledning den internasjonale statistikk-dagen

• Glad, Ingrid Kristine (2025). A Glimpse into Integreat - Norwegian Centre for Knowledge-driven Machine Learning. Full text in Research Archive
• Glad, Ingrid Kristine (2025). Women in AI - Integreat. Full text in Research Archive Show summary

  On research projects in Integreat, especially on anomaly detection, on my way through academia and AI

• Glad, Ingrid Kristine & Frigessi, Arnoldo (2025). Nysgjerrige på: neste generasjon maskinlæring - Nysgjerrige Norge | Acast. Full text in Research Archive Show summary

  Listen to Nysgjerrige på: neste generasjon maskinlæring from Nysgjerrige Norge. Nysgjerrige Norge - Episode 3: Et dypdykk i kunnskapsdrevet maskinlæring med IntegreatI denne episoden tar Kristopher turen til Nils Henrik Abels hus på Blindern i Oslo for å besøke Integreat Senter for Fremragende Forskning. Vi møter professorene og ekteparet Arnoldo Frigessi og Ingrid Glad, som leder det nyoppstartede senteret. De deler visjoner om fremtidens algoritmer, hvor kunnskap og etikk spiller en nøkkelrolle i utviklingen av kunstig intelligens (KI).Nysgjerrige Norge er en serie fra Norges forskningsråd. Sentre for Fremragende Forskning er en støtteordning til landets fremste vitenskapelige miljøer. Du kan lese mer om støtteordningen her. Serien er produsert av Moose Media. Programleder: Kristopher Schau. Musikk: The Dogs. Abonnér på Nysgjerrige Norge og møt flere av landets fremste vitenskapspersonligheter.

• Glad, Ingrid Kristine (2024). Assessing training data importance via Shapley values. Full text in Research Archive
• Borgan, Ørnulf & Glad, Ingrid Kristine (2024). A conversation with Nils Lid Hjort. Scandinavian Journal of Statistics. ISSN 0303-6898. doi: 10.1111/sjos.12732. Full text in Research Archive Show summary

  Professor (now emeritus) Nils Lid Hjort has through more than four decades been one of the most original and productive statisticians in Norway, contributing to a wide range of topics such as survival analysis, Bayesian nonparametrics, empirical likelihood, density estimation, focused inference, model selection, and confidence distributions. This conversation, which took place at the University of Oslo in December 2023, sheds light on how Nils Hjort's curious and open mind, coupled with a deep understanding, has enabled him to seamlessly navigate between different fields of statistics and its applications. Our aim is to encourage the statistics community to always be on the lookout for unexpected connections in statistical science and to embrace unexpected encounters with fellow statisticians from around the world.

• Glad, Ingrid Kristine (2024). Data and knowledge - and me. Full text in Research Archive
• Glad, Ingrid Kristine (2024). “Vi är många, vi är hälften” – men ikke innenfor kunstig intelligens? Full text in Research Archive
• Borgan, Ørnulf & Glad, Ingrid Kristine (2024). A special section honoring Nils Lid Hjort. Scandinavian Journal of Statistics. ISSN 0303-6898. doi: 10.1111/sjos.12745. Full text in Research Archive
• Borgan, Ørnulf & Glad, Ingrid Kristine (2023). Nils Lid Hjort 70: A short walk through a long and splendid career. Full text in Research Archive
• Glad, Ingrid Kristine (2023). Integreat - Norwegian Centre for Knowledge-driven Machine Learning. Full text in Research Archive
• Glad, Ingrid Kristine (2023). Integreat - nytt senter for fremragende forskning. Full text in Research Archive
• Glad, Ingrid Kristine (2023). Integreat - The Norwegian Centre for Knowledge-driven Machine Learning. Full text in Research Archive
• Glad, Ingrid Kristine (2022). Data Science - a science, and a tool. Full text in Research Archive
• Glad, Ingrid Kristine (2022). dScience - Center for Computational and Data Science. Full text in Research Archive
• Glad, Ingrid Kristine (2022). Statistics for the Knowledge Economy. Full text in Research Archive
• Glad, Ingrid Kristine (2022). dScience - en løypemelding. Full text in Research Archive
• Glad, Ingrid Kristine (2021). Hva er så stort med stordata? Full text in Research Archive
• Glad, Ingrid Kristine (2021). BigInsight - Center for Research Based Innovation. Full text in Research Archive
• Glad, Ingrid Kristine (2017). Kompetanseutfordringer knyttet til utnyttelse av stordata i offentlig sektor. Full text in Research Archive
• Glad, Ingrid Kristine (2017). Old bridge still standing. Full text in Research Archive
• Glad, Ingrid Kristine (2017). Hunting high and low - Statistics for Big Data. Full text in Research Archive
• Tveten, Martin; Brandsæter, Andreas & Glad, Ingrid Kristine (2017). Anomaly detection in maritime data streams. Full text in Research Archive
• Glad, Ingrid Kristine (2016). Skaper innovasjon i Data Science. [Internet]. Titan.no. Full text in Research Archive
• Glad, Ingrid Kristine (2016). Hunting high and low. Full text in Research Archive
• Glad, Ingrid Kristine (2016). I spissen for nye innovasjonsklynger. [Internet]. Titan.no. Full text in Research Archive
• Mogstad, Ekaterina; Kristoffersen, Anja Bråthen; Jansen, Peder A. & Glad, Ingrid Kristine (2016). Time to the first medical treatment of farmed salmon to control salmon lice, a survival analysis approach. Full text in Research Archive
• Glad, Ingrid Kristine (2016). Bridging SIRIUS and BigInsight. Full text in Research Archive
• Frigessi, Arnoldo; Buhlmann, Peter; Glad, Ingrid Kristine; Langaas, Mette; Richardson, Sylvia Therese Lamblin & Vannucci, Marina (2016). Abel Symposia: Preface. Abel Symposia. ISSN 2193-2808. 11, p. vii–x. Full text in Research Archive
• Glad, Ingrid Kristine (2016). Sensor Systems. Full text in Research Archive
• Glad, Ingrid Kristine (2016). Brobygging mellom SFI-ene SIRIUS og BigInsight. Full text in Research Archive
• Glad, Ingrid Kristine (2016). Anomalies in multiple sensor series. Full text in Research Archive
• Vanem, Erik; Glad, Ingrid Kristine & Storvik, Geir Olve (2016). Dynamical Linear Models for Condition Monitoring with Multivariate Sensor Data. Full text in Research Archive
• Reppe, Sjur; Lien, Tonje Gulbrandsen; Olstad, Ole Kristoffer; Gautvik, Vigdis Teig; Glad, Ingrid Kristine & Gautvik, Kaare M (2015). Identification of Novel BMD Associated CpGs Using Integrated Genome-Wide Analyses Employing DNA Methylation and Transcript Levels in Bone Biopsies from Postmenopausal Women. Full text in Research Archive
• Glad, Ingrid Kristine & Tharmaratnam, Kukatharmini (2015). Regression. In Engquist, Björn (Eds.), Encyclopedia of Applied and Computational Mathematics. Springer. ISSN 9783540705284. p. 1225–1233. Full text in Research Archive
• Glad, Ingrid Kristine (2015). A highdimensional tribute to Trondheim. Full text in Research Archive
• Glad, Ingrid Kristine (2015). Statistikeren som vil forstå verden - månedens forskerprofil. [Internet]. titan.uio.no. Full text in Research Archive
• Mohammed, Hayat; Antoniadis, Anestis & Glad, Ingrid Kristine (2011). Local comparison of empirical densities via nonparametric regression. Full text in Research Archive
• Scheel, Ida; Aldrin, Magne; Glad, Ingrid Kristine; Sørum, R.; Lyng, Heidi & Frigessi, Arnoldo (2006). The influence of missing value imputation on the detection of differentially expressed genes from microarray data. Full text in Research Archive
• Glad, Ingrid Kristine (2005). Skivebom i dataanalyser. [Journal]. Computerworld. Full text in Research Archive
• Glad, Ingrid Kristine (2005). Daarlig dataanalyse kan koste dyrt. [Journal]. Apollon. Full text in Research Archive
• Scheel, Ida; Aldrin, Magne; Glad, Ingrid Kristine; Sørum, Ragnhild; Lyng, Heidi & Frigessi, Arnoldo (2005). The influence of missing value imputation on the detection of differentially expressed genes from microarray data. Full text in Research Archive
• Scheel, Ida; Aldrin, Magne; Glad, Ingrid Kristine; Sørum, R.; Lyng, Heidi & Frigessi, Arnoldo (2005). The influence of missing value imputation on the detection of differentially expressed genes from microarray data. Full text in Research Archive
• Glad, Ingrid Kristine (2005). Genomewide estimation of absolute transcript concentrations from spotted microarray data. Full text in Research Archive
• Glad, Ingrid Kristine (2004). Gud eksisterer (om bayesiansk statistikk). [Journal]. Dagbladet. Full text in Research Archive
• Baumbusch, Lars; Lingjærde, Ole Christian; Liestøl, Knut; Glad, Ingrid Kristine; Pollack, John & Chin, K. [Show all 11 contributors for this article] (2003). A comparison of BAC- and cDNA-arrays for the detection of copy number alterations in human breast tumors. Full text in Research Archive
• Glad, Ingrid Kristine (2003). Bayesiansk analyse av cDNA-mikromatrise-data. Full text in Research Archive
• Baumbusch, Lars; Lingjærde, Ole Christian; Liestøl, Knut; Glad, Ingrid Kristine; Pollack, John & Chin, K. [Show all 11 contributors for this article] (2003). A comparison of BAC- and cDNA-arrays for the detection of copy number alterations in human breast tumors. Full text in Research Archive
• Glad, Ingrid Kristine (2002). Towards a comprehensive statistical model of cDNA microarray experiments. Full text in Research Archive
• Glad, Ingrid Kristine (2002). Towards a comprehensive statistical model for cDNA microarray experiments. Full text in Research Archive
• Glad, Ingrid Kristine (2001). Nonparametric density estimation using the sinc kernel: finite sample analysis. Full text in Research Archive
• Glad, Ingrid Kristine (2000). Å se på ting vi ikke kan se. Full text in Research Archive
• Glad, Ingrid Kristine (2000). Å se på ting vi ikke kan se. Full text in Research Archive
• Glad, Ingrid Kristine (1999). Book Review (Lindsey, J.K.: Introductory Statistics: A Modelling Approach). Statistics in Medicine. ISSN 0277-6715. 18, p. 759–759. Full text in Research Archive
• Glad, Ingrid Kristine (1998). Edge preserving M-smoothing. Full text in Research Archive
• Glad, Ingrid Kristine (1998). Using HIV seropositivity notifications in back-calculation for the AIDS epidemic. Full text in Research Archive
• Glad, Ingrid Kristine (1998). Bayesian back-calculation with HIV seropositivity notifications. Full text in Research Archive
• Glad, Ingrid Kristine (1998). Aa regne med AIDS. Full text in Research Archive
• Glad, Ingrid Kristine (1996). Brev fra Roma: veni, vidi,... vici? Tilfeldig gang. ISSN 0803-8953. 13(4). Full text in Research Archive
• Tveten, Martin; Glad, Ingrid Kristine & Hjort, Nils Lid (2021). Scalable change and anomaly detection in cross-correlated data. Universitetet i Oslo. Full text in Research Archive
• Tveten, Martin & Glad, Ingrid Kristine (2019). Online Detection of Sparse Changes in High-Dimensional Data Streams Using Tailored Projections. arXiv. Full text in Research Archive
• Holden, Marit; Glad, Ingrid Kristine; Hauge, Håvard Hildeng; Hovig, Eivind & Liestøl, Knut (2012). Image restoration and analysis of biomolecular radioactivity images II. Norsk Regnesentral. Full text in Research Archive
• Holden, Marit; Glad, Ingrid Kristine; Hauge, Håvard Lilledalen & Liestøl, Knut (2008). Image restoration and analysis of biomolecular radioactivity images. Norsk Regnesentral. Full text in Research Archive
• Frigessi, Arnoldo; Wiel, Mark Van der; Holden, Marit; Glad, Ingrid Kristine; Lyng, Heidi & Wiel, Mark A. van de [Show all 7 contributors for this article] (2004). Model-based estimation of transcript concentrations from spotted microarray data. Norsk Regnesentral. ISSN 9788253905075. Full text in Research Archive Show summary

  In this paper a bootstrap algorithm Much data from spotted microarrays remain unused because obtained with different protocols, platforms or designs, making comparisons across experiments impossible. We have developed a model-based method, which provides absolute transcript levels. Transcript levels are universal, and can be included in further analyses with similar estimates obtained with different techniques in other laboratories. It is a first step both towards genuine meta-analyses, including comparisons across different organisms, and the building of data bases of transcript levels in cells. Our method is based on statistical modelling incorporating all available information about the experiment, from target preparation to image analysis, coherently propagating uncertainties from data to estimates. It requires some genes spotted in replicates, their number being related to the levels of experimental factors included in the model, but not to the number of spotted genes. No uncertainty in the estimates caused by decimated data sets, indirect comparisons, normalisation or imputation of missing values, is introduced, leading to a far more precise analysis of microarray data than provided by conventional methods. Using a flexible Bayesian technique we estimate the highly multivariate joint posterior distribution of all transcripts, which enables extended exploitation of the data. In the present work we apply our method to cervical cancer data. We show that the estimated transcript concentrations are accurate and reproducible, and demonstrate improved statistical tools for selecting genes based on their concentration in highly unbalanced experimental settings.

• Glad, Ingrid Kristine; Hjort, Nils Lid & Ushakov, N.G. (1999). Correction of density estimators which are not densities. Universitetet i Oslo. ISSN 9788255312147. Full text in Research Archive
• Glad, Ingrid Kristine; Hjort, Nils Lid & Ushakov, N.G. (1999). Density estimation using the sinc kernel. Universitetet i Oslo. ISSN 9788255312161. Full text in Research Archive
• Glad, Ingrid Kristine; Frigessi, Arnoldo; Tomba, Gianpaolo Scalia; Balducci, Maria & Pezzotti, Patrizio (1998). Bayesian back-calculations with HIV seropositivity notifications. Universitetet i Oslo. Full text in Research Archive
• Hjort, Nils Lid & Glad, Ingrid Kristine (1996). Exact performance of a semiparametric density estimator for normal mixture truths. Universitetet i Oslo. ISSN 9788255310297. Full text in Research Archive
• Glad, Ingrid Kristine (1996). Parametrically guided nonparametric regression. Universitetet i Oslo. ISSN 9788255310594. Full text in Research Archive
• Glad, Ingrid Kristine (1996). A note on unconditional properties of a parametrically guided Nadaraya-Watson estimator. Universitetet i Oslo. ISSN 9788255310600. Full text in Research Archive
• Glad, Ingrid Kristine (1995). Contributions to nonparametric function estimation and Bayesian image analysis. Norges Tekniske Hoeyskole NTH. ISSN 9788271198060. Full text in Research Archive
• Hjort, Nils Lid & Glad, Ingrid Kristine (1994). Nonparametric Density Estimation With a Parametric Start. Universitetet i Oslo. ISSN 9788255308850. Full text in Research Archive

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