Ingrid Kristine Glad

• 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.  ISSN 1755-8794.  11(24) . doi: 10.1186/s12920-018-0341-2
• Brandsæter, Andreas; Vanem, Erik & Glad, Ingrid Kristine (2017). Cluster Based Anomaly Detection with Applications in the Maritime Industry, In Chuan Li; José Valente de Oliveira; Ping Ding; Wei Guo; Juanjuan Shi & Yun Bai (ed.),  Proceedings of the 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control SDPC 2017.  IEEE.  ISBN 978-1-5090-4020-9.  Konferanseartikkel.  s 328 - 333
• 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.  ISSN 1471-2105.  18:263, s 1- 8 . doi: 10.1186/s12859-017-1678-9 Full text in Research Archive.
• Reppe, Sjur; Lien, Tonje Gulbrandsen; Hsu, Yi-Hsiang; Gautvik, Vigdis Teig; Olstad, Ole Kristoffer; Yu, Rona; Bakke, Hege Gilbø; Lyle, Robert; Kringen, Marianne K.; Glad, Ingrid Kristine & Gautvik, Kaare M (2017). Distinct DNA methylation profiles in bone and blood of osteoporotic and healthy postmenopausal women. Epigenetics.  ISSN 1559-2294.  12(8), s 674- 687 . doi: 10.1080/15592294.2017.1345832
• Simovski, Boris; Vodak, Daniel; Gundersen, Sveinung; Domanska, Diana Ewa; Azab, Abdulrahman; Holden, Lars; Holden, Marit; Grytten, Ivar; Rand, Knut Dagestad; Drabløs, Finn; Johansen, Morten; Mora Ortiz, Antonio Carlos; Lund-Andersen, Christin; Fromm, Bastian; Eskeland, Ragnhild; Gabrielsen, Odd Stokke; Ferkingstad, Egil; Nakken, Sigve; Bengtsen, Mads; Nederbragt, Alexander Johan; Thorarensen, Hildur Sif; Akse, Johannes Andreas; Glad, Ingrid Kristine; Hovig, Johannes Eivind & Sandve, Geir Kjetil (2017). GSuite HyperBrowser: integrative analysis of dataset collections across the genome and epigenome. GigaScience.  ISSN 2047-217X.  6(7), s 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

• Brandsæter, Andreas; Manno, Gabriele; Vanem, Erik & Glad, Ingrid Kristine (2016). An Application of Sensor-Based Anomaly Detection in the Maritime Industry, In NA NA (ed.),  2016 IEEE International Conference on Prognostics and Health Management (ICPHM 2016).  Curran Associates, Inc..  ISBN 9781509003839.  artikkel.
• Frigessi, Arnoldo; Buhlmann, Peter; Glad, Ingrid Kristine; Richardson, Sylvia & Vannucci, Marina (2016). Some themes in high-dimensional statistics. Abel Symposia.  ISSN 2193-2808.  11, s 1- 13 . doi: 10.1007/978-3-319-27099-9_1
• Glad, Ingrid Kristine & Hjort, Nils Lid (2016). Model uncertainty first, not afterwards. Statistical Science.  ISSN 0883-4237.  31(4), s 490- 494 . doi: 10.1214/16-STS559
• 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, s 37- 66 . doi: 10.1007/978-3-319-27099-9_3
• Antoniadis, Anestis; Glad, Ingrid Kristine & Mohammed, Hayat (2015). Local comparison of empirical distributions via nonparametric regression. Journal of Statistical Computation and Simulation.  ISSN 0094-9655.  85(12), s 2384- 2405 . doi: 10.1080/00949655.2014.929133
• Bergersen, Linn Cecilie; Tharmaratnam, Kukatharmini & Glad, Ingrid Kristine (2014). Monotone Splines Lasso. Computational Statistics & Data Analysis.  ISSN 0167-9473.  77, s 336- 351 . doi: 10.1016/j.csda.2014.03.013
• Sandve, Geir Kjetil; Gundersen, Sveinung; Johansen, Morten; Glad, Ingrid Kristine; Gunathasan, Krishanthi; Holden, Lars; Holden, Marit; Liestøl, Knut; Nygård, Ståle; Nygaard, Vegard; Paulsen, Jonas; Rydbeck, Halfdan; Trengereid, Kai; Clancy, Trevor; Drabløs, Finn; Ferkingstad, Egil; Kalaš, Matúš; Lien, Tonje Gulbrandsen; Rye, Morten Beck; Frigessi, Arnoldo & Hovig, Johannes Eivind (2013). The Genomic HyperBrowser: an analysis web server for genome-scale data. Nucleic Acids Research.  ISSN 0305-1048.  41(W1), s 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 & Hovig, Johannes Eivind (2013). Handling realistic assumptions in hypothesis testing of 3D co-localization of genomic elements. Nucleic Acids Research.  ISSN 0305-1048.  41(10), s 5164- 5174 . doi: 10.1093/nar/gkt227 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.

• di Serio, C; 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 E Biganzoli; A Vellido; F Ambrogi & R Tagliaferri (ed.),  Computational Intelligence methods for bioinformatics and biostatistics.  Springer.  ISBN 978-3-642-35686-5.  Lectures in Computer Science vol 7548.  s 10 - 20
• Sandve, Geir Kjetil; Gundersen, Sveinung; Rydbeck, Halfdan; Glad, Ingrid Kristine; Holden, Lars; Holden, Marit; Liestøl, Knut; Clancy, Trevor; Drabløs, Finn; Ferkingstad, Egil; Johansen, Morten; Nygaard, Vegard; Tøstesen, Eivind; Frigessi, Arnoldo & Hovig, Eivind (2011). The differential disease regulome. BMC Genomics.  ISSN 1471-2164.  12, s 353 . doi: 10.1186/1471-2164-12-353 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

• Ambrosi, Alessandro; Glad, Ingrid Kristine; Pellin, Danilo; Cattoglio, Claudia; Mavilio, Fulvio; di serio, clelia & Frigessi, Arnoldo (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
• Bergersen, Linn Cecilie; Glad, Ingrid Kristine & Lyng, Heidi (2011). Weighted Lasso with Data Integration. Statistical Applications in Genetics and Molecular Biology.  ISSN 1544-6115.  10(1) . doi: 10.2202/1544-6115.1703
• Sandve, Geir Kjetil; Gundersen, Sveinung; Rydbeck, Halfdan; Glad, Ingrid Kristine; Holden, Lars; Holden, Marit; Liestøl, Knut; Clancy, Trevor; Ferkingstad, Egil; Johansen, Morten; Nygaard, Vegard; Tøstesen, Eivind; Frigessi, Arnoldo & Hovig, Eivind (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; Svendsrud, Debbie H.; Stokke, Trond & Sundfør, Kolbein (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 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; Cepko, Connie L.; Frigessi, Arnoldo; Glad, Ingrid K.; Wiel, Mark A. van de; Hovig, Eivind & Lyng, H (2008). Validation of oligoarrays for quantitative exploration of the transcriptome. BMC Genomics.  ISSN 1471-2164.  9 . doi: 10.1186/1471-2164-9-258 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), s 5977- 5983
• van de Wiel, Mark; Holden, Marit; Glad, Ingrid Kristine; Lyng, Heidi & Frigessi, Arnoldo (2006). Bayesian process-based modeling of two-channel microarray experiments estimating absolute mRNA concentrations, In Kim-Anh Do; Peter Müller & M. Vannucci (ed.),  Bayesian Inference for Gene Expression and Proteomics.  Cambridge University Press.  ISBN 978-0-521-86092-5.  Kapittel.  s 75 - 97
• Frigessi, Arnoldo; van de Wiel, Mark; 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.  ISSN 0305-1048.  33(17) . doi: 10.1093/nar/gni141 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.

• Kristensen, Vessela Nedelcheva; Sørlie, Therese; Sorlie, Therese; Geisler, Jürgen; Geisler, E; Yoshimura, Noriko; Lingjærde, Ole Christian; Linegjaerde, Ole-Christian; Glad, Ingrid; Frigessi, Arnoldo; Harada, Nobuhiro; Lonning, PE; Lønning, Per Eystein & Børresen-Dale, Anne-Lise (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), s 105- 111 . doi: 10.1016/j.jsbmb.2005.04.028
• Lingjærde, Ole Christian; Baumbusch, Lars Oliver; Liestøl, Knut; Glad, Ingrid Kristine & Børresen-Dale, Anne-Lise (2005). CGH-Explorer: a program for analysis of array-CGH data. Bioinformatics.  ISSN 1367-4803.  21 . doi: 10.1093/bioinformatics/bti113
• 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), s 4272- 4279
• 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), s 415- 427
• 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), s 553- 556
• 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), s 101- 108
• Glad, Ingrid Kristine (1998). Bayesian projection of the acquired immune deficiency syndrome epidemic - Discussion. JRSS-C (Applied Statistics).  47(4)
• Glad, Ingrid Kristine (1998). Parametrically guided nonparametric regression. Scandinavian Journal of Statistics.  ISSN 0303-6898.  25(4), s 649- 668
• Glad, Ingrid Kristine & Sebastiani, Giovanni (1995). A Bayesian approach to synthetic magnetic resonance imaging. Biometrika.  ISSN 0006-3444.  82(2), s 237- 250
• Hjort, Nils Lid & Glad, Ingrid Kristine (1995). Nonparametric density estimation with a parametric start. Annals of Statistics.  ISSN 0090-5364.  23(3), s 882- 904
• Glad, Ingrid Kristine (1994). Bayesian Prediction of MR Images, In R. Dutter & W. Grossmann (ed.),  Short Communications in Computational Statistics.  Physica Verlag, Heidelberg.  s 59 - 60
• 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), s 169- 173

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• Frigessi, Arnoldo; Bühlmann, Peter; Glad, Ingrid Kristine; Langaas, Mette; Richardson, Sylvia Therese Lamblin & Vannucci, Marina (ed.) (2016). Statistical Analysis for High-Dimensional Data. Springer.  ISBN 978-3-319-27097-5.  306 s.

View all works in Cristin

• Glad, Ingrid Kristine (2017). Hunting high and low - Statistics for Big Data.
• Glad, Ingrid Kristine (2017). Kompetanseutfordringer knyttet til utnyttelse av stordata i offentlig sektor.
• Glad, Ingrid Kristine (2017). Old bridge still standing.
• Tveten, Martin; Brandsæter, Andreas & Glad, Ingrid Kristine (2017). Anomaly detection in maritime data streams.
• 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, s vii- x
• Glad, Ingrid Kristine (2016). Anomalies in multiple sensor series.
• Glad, Ingrid Kristine (2016). Bridging SIRIUS and BigInsight.
• Glad, Ingrid Kristine (2016). Brobygging mellom SFI-ene SIRIUS og BigInsight.
• Glad, Ingrid Kristine (2016). Hunting high and low.
• Glad, Ingrid Kristine (2016, 17. mars). I spissen for nye innovasjonsklynger. [Internett].  Titan.no.
• Glad, Ingrid Kristine (2016). Sensor Systems.
• Glad, Ingrid Kristine (2016, 06. desember). Skaper innovasjon i Data Science. [Internett].  Titan.no.
• 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.
• Vanem, Erik; Glad, Ingrid Kristine & Storvik, Geir Olve (2016). Dynamical Linear Models for Condition Monitoring with Multivariate Sensor Data.
• Glad, Ingrid Kristine (2015). A highdimensional tribute to Trondheim.
• Glad, Ingrid Kristine (2015, 25. november). Statistikeren som vil forstå verden - månedens forskerprofil. [Internett].  titan.uio.no.
• Glad, Ingrid Kristine & Tharmaratnam, Kukatharmini (2015). Regression, In Björn Engquist (ed.),  Encyclopedia of Applied and Computational Mathematics.  Springer.  ISBN 978-3-540-70528-4.  Kapittel.  s 1225 - 1233
• 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..
• Holden, Marit; Glad, Ingrid Kristine; Hauge, Håvard Hildeng; Hovig, Eivind & Liestøl, Knut (2012). Image restoration and analysis of biomolecular radioactivity images II. NR-notat. SAMBA/11/12.
• Mohammed, Hayat; Antoniadis, Anestis & Glad, Ingrid Kristine (2011). Local comparison of empirical densities via nonparametric regression.
• Holden, Marit; Glad, Ingrid Kristine; Hauge, Håvard Lilledalen & Liestøl, Knut (2008). Image restoration and analysis of biomolecular radioactivity images. NR-notat. SAMBA/27/08.
• 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.
• Glad, Ingrid Kristine (2005, 14. mars). Daarlig dataanalyse kan koste dyrt.  Apollon.
• Glad, Ingrid Kristine (2005). Genomewide estimation of absolute transcript concentrations from spotted microarray data.
• Glad, Ingrid Kristine (2005, 14. mars). Skivebom i dataanalyser.  Computerworld.
• 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.
• 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.
• Glad, Ingrid Kristine (2004, 25. mars). Gud eksisterer (om bayesiansk statistikk).  Dagbladet.
• Frigessi, Arnoldo; Van der Wiel, Mark; Holden, Marit; Glad, Ingrid Kristine; Lyng, Heidi; Wiel, Mark A. van de & Glad, Ingrid K. (2004). Model-based estimation of transcript concentrations from spotted microarray data. Report at the Norwegian Computing Center. 999.
• Baumbusch, Lars; Lingjærde, Ole Christian; Liestøl, Knut; Glad, Ingrid Kristine; Pollack, John; Chin, K.; Sørlie, Therese; Brown, Patrick; Botstein, David; Gray, Joe & Børresen-Dale, Anne-Lise (2003). A comparison of BAC- and cDNA-arrays for the detection of copy number alterations in human breast tumors.
• Baumbusch, Lars; Lingjærde, Ole Christian; Liestøl, Knut; Glad, Ingrid Kristine; Pollack, John; Chin, K.; Sørlie, Therese; Brown, Patrick; Botstein, David; Gray, Joe & Børresen-Dale, Anne-Lise (2003). A comparison of BAC- and cDNA-arrays for the detection of copy number alterations in human breast tumors.
• Glad, Ingrid Kristine (2003). Bayesiansk analyse av cDNA-mikromatrise-data.
• Glad, Ingrid Kristine (2002). Towards a comprehensive statistical model for cDNA microarray experiments.
• Glad, Ingrid Kristine (2002). Towards a comprehensive statistical model of cDNA microarray experiments.
• Glad, Ingrid Kristine (2001). Nonparametric density estimation using the sinc kernel: finite sample analysis.
• Glad, Ingrid Kristine (2000). Å se på ting vi ikke kan se.
• Glad, Ingrid Kristine (2000). Å se på ting vi ikke kan se.
• Glad, Ingrid Kristine (1999). Book Review (Lindsey, J.K.: Introductory Statistics: A Modelling Approach). Statistics in Medicine.  ISSN 0277-6715.  18, s 759- 759
• Glad, Ingrid Kristine; Hjort, Nils Lid & Ushakov, N.G. (1999). Correction of density estimators which are not densities. Statistical research report (Universitetet i Oslo. Matematisk institut. 17.
• Glad, Ingrid Kristine; Hjort, Nils Lid & Ushakov, N.G. (1999). Density estimation using the sinc kernel. Statistical research report (Universitetet i Oslo. Matematisk institut. 19.
• Glad, Ingrid Kristine (1998). Aa regne med AIDS.
• Glad, Ingrid Kristine (1998). Bayesian back-calculation with HIV seropositivity notifications.
• Glad, Ingrid Kristine (1998). Edge preserving M-smoothing.
• Glad, Ingrid Kristine (1998). Using HIV seropositivity notifications in back-calculation for the AIDS epidemic.
• Glad, Ingrid Kristine; Balducci, M.; Tomba, G. Scalia; Frigessi, A. & Pezzotti, P. (1998). Bayesian backcalculation with HIV seropositivity notifications. Statistical research report (Universitetet i Oslo. Matematisk institut. 4.
• Glad, Ingrid Kristine (1996). A note on unconditional properties of a parametrically guided Nadaraya-Watson estimator. Statistical research report (Universitetet i Oslo. Matematisk institut. 19.
• Glad, Ingrid Kristine (1996). Brev fra Roma: veni, vidi,... vici?. Tilfeldig gang.  ISSN 0803-8953.  13(4)
• Glad, Ingrid Kristine (1996). Parametrically guided nonparametric regression. Statistical research report (Universitetet i Oslo. Matematisk institut. 18.
• Hjort, Nils Lid & Glad, Ingrid Kristine (1996). Exact performance of a semiparametric density estimator for normal mixture truths. Statistical research report (Universitetet i Oslo. Matematisk institut. 8.
• Glad, Ingrid Kristine (1995). Contributions to nonparametric function estimation and Bayesian image analysis. Doktor Ingenioeravhandling. 1995:59.
• Hjort, Nils Lid & Glad, Ingrid Kristine (1994). Nonparametric Density Estimation With a Parametric Start. Statistical research report (Universitetet i Oslo. Matematisk institut. 2.

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