{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T04:31:53Z","timestamp":1775190713535,"version":"3.50.1"},"reference-count":46,"publisher":"Oxford University Press (OUP)","issue":"12","license":[{"start":{"date-parts":[[2016,10,2]],"date-time":"2016-10-02T00:00:00Z","timestamp":1475366400000},"content-version":"vor","delay-in-days":2315,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/2.0\/uk\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2010,6,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: High-throughput data is providing a comprehensive view of the molecular changes in cancer tissues. New technologies allow for the simultaneous genome-wide assay of the state of genome copy number variation, gene expression, DNA methylation and epigenetics of tumor samples and cancer cell lines.<\/jats:p><jats:p>Analyses of current data sets find that genetic alterations between patients can differ but often involve common pathways. It is therefore critical to identify relevant pathways involved in cancer progression and detect how they are altered in different patients.<\/jats:p><jats:p>Results: We present a novel method for inferring patient-specific genetic activities incorporating curated pathway interactions among genes. A gene is modeled by a factor graph as a set of interconnected variables encoding the expression and known activity of a gene and its products, allowing the incorporation of many types of omic data as evidence. The method predicts the degree to which a pathway's activities (e.g. internal gene states, interactions or high-level \u2018outputs\u2019) are altered in the patient using probabilistic inference.<\/jats:p><jats:p>Compared with a competing pathway activity inference approach called SPIA, our method identifies altered activities in cancer-related pathways with fewer false-positives in both a glioblastoma multiform (GBM) and a breast cancer dataset. PARADIGM identified consistent pathway-level activities for subsets of the GBM patients that are overlooked when genes are considered in isolation. Further, grouping GBM patients based on their significant pathway perturbations divides them into clinically-relevant subgroups having significantly different survival outcomes. These findings suggest that therapeutics might be chosen that target genes at critical points in the commonly perturbed pathway(s) of a group of patients.<\/jats:p><jats:p>Availability:Source code available at http:\/\/sbenz.github.com\/Paradigm<\/jats:p><jats:p>Contact: \u00a0jstuart@soe.ucsc.edu<\/jats:p><jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btq182","type":"journal-article","created":{"date-parts":[[2010,6,7]],"date-time":"2010-06-07T07:28:13Z","timestamp":1275895693000},"page":"i237-i245","source":"Crossref","is-referenced-by-count":693,"title":["Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM"],"prefix":"10.1093","volume":"26","author":[{"given":"Charles J.","family":"Vaske","sequence":"first","affiliation":[{"name":"1 Howard Hughes Medical Institute and 2 Department of Biomolecular Engineering and Center for Biomolecular Science and Engineering, UC Santa Cruz, CA, USA"}]},{"given":"Stephen C.","family":"Benz","sequence":"additional","affiliation":[{"name":"1 Howard Hughes Medical Institute and 2 Department of Biomolecular Engineering and Center for Biomolecular Science and Engineering, UC Santa Cruz, CA, USA"}]},{"given":"J. Zachary","family":"Sanborn","sequence":"additional","affiliation":[{"name":"1 Howard Hughes Medical Institute and 2 Department of Biomolecular Engineering and Center for Biomolecular Science and Engineering, UC Santa Cruz, CA, USA"}]},{"given":"Dent","family":"Earl","sequence":"additional","affiliation":[{"name":"1 Howard Hughes Medical Institute and 2 Department of Biomolecular Engineering and Center for Biomolecular Science and Engineering, UC Santa Cruz, CA, USA"}]},{"given":"Christopher","family":"Szeto","sequence":"additional","affiliation":[{"name":"1 Howard Hughes Medical Institute and 2 Department of Biomolecular Engineering and Center for Biomolecular Science and Engineering, UC Santa Cruz, CA, USA"}]},{"given":"Jingchun","family":"Zhu","sequence":"additional","affiliation":[{"name":"1 Howard Hughes Medical Institute and 2 Department of Biomolecular Engineering and Center for Biomolecular Science and Engineering, UC Santa Cruz, CA, USA"}]},{"given":"David","family":"Haussler","sequence":"additional","affiliation":[{"name":"1 Howard Hughes Medical Institute and 2 Department of Biomolecular Engineering and Center for Biomolecular Science and Engineering, UC Santa Cruz, CA, USA"},{"name":"1 Howard Hughes Medical Institute and 2 Department of Biomolecular Engineering and Center for Biomolecular Science and Engineering, UC Santa Cruz, CA, USA"}]},{"given":"Joshua M.","family":"Stuart","sequence":"additional","affiliation":[{"name":"1 Howard Hughes Medical Institute and 2 Department of Biomolecular Engineering and Center for Biomolecular Science and Engineering, UC Santa Cruz, CA, USA"}]}],"member":"286","published-online":{"date-parts":[[2010,6,1]]},"reference":[{"key":"2023012508052910700_B1","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1038\/35000501","article-title":"Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling","volume":"403","author":"Alizadeh","year":"2000","journal-title":"Nature"},{"key":"2023012508052910700_B2","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1038\/nrg1749","article-title":"Microarray data analysis: from disarray to consolidation and consensus","volume":"7","author":"Allison","year":"2006","journal-title":"Nat. Rev. Genet."},{"key":"2023012508052910700_B3","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1038\/75556","article-title":"Gene ontology: tool for the unification of biology. the gene ontology consortium","volume":"25","author":"Ashburner","year":"2000","journal-title":"Nat. Genet."},{"key":"2023012508052910700_B4","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/S0092-8674(04)00304-6","article-title":"Predicting gene expression from sequence","volume":"117","author":"Beer","year":"2004","journal-title":"Cell"},{"key":"2023012508052910700_B5","article-title":"BioPAX\u2013biological pathways exchange language","author":"BioPAX working group","year":"2004","journal-title":"Documentation"},{"key":"2023012508052910700_B6","doi-asserted-by":"crossref","first-page":"R215","DOI":"10.1186\/gb-2007-8-10-r215","article-title":"High-resolution ACGH and expression profiling identifies a novel genomic subtype of ER negative breast cancer","volume":"8","author":"Chin","year":"2007","journal-title":"Genome Biol."},{"key":"2023012508052910700_B7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","article-title":"Maximum likelihood from incomplete data via the EM algorithm","volume":"39","author":"Dempster","year":"1977","journal-title":"J. Roy. Stat. Soc. Series B (Methodol.)"},{"key":"2023012508052910700_B8","doi-asserted-by":"crossref","DOI":"10.1186\/gb-2002-3-7-research0036","article-title":"A prediction-based resampling method for estimating the number of clusters in a dataset","volume":"3","author":"Dudoit","year":"2002","journal-title":"Genome Biol."},{"key":"2023012508052910700_B9","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0000425","article-title":"Identification of key processes underlying cancer phenotypes using biological pathway analysis","volume":"2","author":"Efroni","year":"2007","journal-title":"PLoS ONE"},{"key":"2023012508052910700_B10","doi-asserted-by":"crossref","first-page":"14863","DOI":"10.1073\/pnas.95.25.14863","article-title":"Cluster analysis and display of genome-wide expression patterns","volume":"95","author":"Eisen","year":"1998","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"2023012508052910700_B11","first-page":"165","article-title":"Sequential update of bayesian network structure","volume-title":"Procedings of the 13th Conference on Uncertainty in Artificial Intelligence (UAI'97)","author":"Friedman","year":"1997"},{"key":"2023012508052910700_B12","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1126\/science.1094068","article-title":"Inferring cellular networks using probabilistic graphical models","volume":"303","author":"Friedman","year":"2004","journal-title":"Science"},{"key":"2023012508052910700_B13","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1101\/gr.5750507","article-title":"Refinement and expansion of signaling pathways: the osmotic response network in yeast","volume":"17","author":"Gat-Viks","year":"2007","journal-title":"Genome Res."},{"key":"2023012508052910700_B14","first-page":"31","article-title":"The factor graph network model for biological systems","volume-title":"RECOMB","author":"Gat-Viks","year":"2005"},{"key":"2023012508052910700_B15","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1089\/cmb.2006.13.165","article-title":"A probabilistic methodology for integrating knowledge and experiments on biological networks","volume":"13","author":"Gat-Viks","year":"2006","journal-title":"J. Computat. Biol."},{"key":"2023012508052910700_B16","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1126\/science.286.5439.531","article-title":"Molecular classification of cancer: class discovery and class prediction by gene expression monitoring","volume":"286","author":"Golub","year":"1999","journal-title":"Science"},{"key":"2023012508052910700_B17","doi-asserted-by":"crossref","first-page":"D428","DOI":"10.1093\/nar\/gki072","article-title":"Reactome: a knowledgebase of biological pathways","volume":"33","author":"Joshi-Tope","year":"2005","journal-title":"Nucleic Acids Res."},{"key":"2023012508052910700_B18","doi-asserted-by":"crossref","first-page":"7438","DOI":"10.1073\/pnas.0605874104","article-title":"Akt1 governs breast cancer progression in vivo","volume":"104","author":"Ju","year":"2007","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"2023012508052910700_B19","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1089\/10665270050514954","article-title":"Analysis of variance for gene expression microarray data","volume":"7","author":"Kerr","year":"2000","journal-title":"J. Comput. Biol."},{"key":"2023012508052910700_B20","first-page":"498","article-title":"Factor graphs and the sum-product algorithm","volume":"47","author":"Kschischang","year":"2001","journal-title":"IEEETIT: IEEE Trans. Inf. Theory"},{"key":"2023012508052910700_B21","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1677\/erc.0.0080083","article-title":"EGF mutant receptor VIII as a molecular target in cancer therapy","volume":"8","author":"Kuan","year":"2001","journal-title":"Endocr. Relat. Cancer"},{"key":"2023012508052910700_B22","doi-asserted-by":"crossref","first-page":"14062","DOI":"10.1073\/pnas.0601852103","article-title":"Identifying regulatory mechanisms using individual variation reveals key role for chromatin modification","volume":"103","author":"Lee","year":"2006","journal-title":"Proc. Nati Acad. Sci. USA"},{"key":"2023012508052910700_B23","doi-asserted-by":"crossref","first-page":"3593","DOI":"10.1158\/0008-5472.CAN-05-2912","article-title":"FoxM1B is overexpressed in human glioblastomas and critically regulates the tumorigenicity of glioma cells","volume":"66","author":"Liu","year":"2006","journal-title":"Cancer Res."},{"key":"2023012508052910700_B24","author":"Mooij","year":"2009","journal-title":"libDAI 0.2.3: A free\/open source C++ library for Discrete Approximate Inference."},{"key":"2023012508052910700_B25","first-page":"467","article-title":"Loopy belief propagation for approximate inference: an empirical study","volume-title":"Proceedings of 15th Conference on Uncertainity in Artifical Intelligence","author":"Murphy","year":"1999"},{"key":"2023012508052910700_B26","doi-asserted-by":"crossref","first-page":"1507","DOI":"10.1038\/sj.onc.1209920","article-title":"A gene-expression signature to predict survival in breast cancer across independent data sets","volume":"26","author":"Naderi","year":"2007","journal-title":"Oncogene"},{"key":"2023012508052910700_B27","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1093\/nar\/27.1.29","article-title":"Kegg: Kyoto encyclopedia of genes and genomes","volume":"27","author":"Ogata","year":"1999","journal-title":"Nucleic Acids Res."},{"key":"2023012508052910700_B28","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1093\/biostatistics\/kxh008","article-title":"Circular binary segmentation for the analysis of array-based DNA copy number data","volume":"5","author":"Olshen","year":"2004","journal-title":"Biostatistics"},{"key":"2023012508052910700_B29","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1093\/bioinformatics\/bti115","article-title":"The MIPS mammalian protein-protein interaction database","volume":"21","author":"Pagel","year":"2005","journal-title":"Bioinformatics"},{"key":"2023012508052910700_B30","doi-asserted-by":"crossref","first-page":"392","DOI":"10.3816\/CBC.2008.n.047","article-title":"Unraveling the biologic and clinical complexities of HER2","volume":"8","author":"Park","year":"2008","journal-title":"Clin. Breast Cancer"},{"key":"2023012508052910700_B31","doi-asserted-by":"crossref","first-page":"1807","DOI":"10.1126\/science.1164382","article-title":"An integrated genomic analysis of human glioblastoma multiforme","volume":"321","author":"Parsons","year":"2008","journal-title":"Science"},{"key":"2023012508052910700_B32","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1126\/science.1105809","article-title":"Causal protein-signaling networks derived from multiparameter single-cell data","volume":"308","author":"Sachs","year":"2005","journal-title":"Science"},{"key":"2023012508052910700_B33","doi-asserted-by":"crossref","first-page":"D674","DOI":"10.1093\/nar\/gkn653","article-title":"PID: the pathway interaction database","volume":"37","author":"Schaefer","year":"2009","journal-title":"Nucleic Acids Res"},{"issue":"Suppl 6","key":"2023012508052910700_B34","doi-asserted-by":"crossref","first-page":"S38","DOI":"10.1038\/ng1561","article-title":"From signatures to models: understanding cancer using microarrays","volume":"37","author":"Segal","year":"2005","journal-title":"Nat. Genet"},{"key":"2023012508052910700_B35","doi-asserted-by":"crossref","first-page":"1983","DOI":"10.1101\/gad.14.16.1983","article-title":"HIF-1 and human disease: one highly involved factor","volume":"14","author":"Semenza","year":"2000","journal-title":"Genes Dev"},{"key":"2023012508052910700_B36","first-page":"149","article-title":"Statistical methods for identifying differentially expressed genes in DNA microarrays","volume":"224","author":"Storey","year":"2003","journal-title":"Methods Mol. Biol."},{"key":"2023012508052910700_B37","doi-asserted-by":"crossref","first-page":"15545","DOI":"10.1073\/pnas.0506580102","article-title":"Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles","volume":"102","author":"Subramanian","year":"2005","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"2023012508052910700_B38","doi-asserted-by":"crossref","first-page":"2907","DOI":"10.1073\/pnas.96.6.2907","article-title":"Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation","volume":"96","author":"Tamayo","year":"1999","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"2023012508052910700_B39","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1093\/bioinformatics\/btn577","article-title":"A novel signaling pathway impact analysis","volume":"25","author":"Tarca","year":"2009","journal-title":"Bioinformatics"},{"key":"2023012508052910700_B40","doi-asserted-by":"crossref","first-page":"1061","DOI":"10.1038\/nature07385","article-title":"Comprehensive genomic characterization defines human glioblastoma genes and core pathways","volume":"455","author":"TCGA","year":"2008","journal-title":"Nature"},{"key":"2023012508052910700_B41","doi-asserted-by":"crossref","first-page":"1454","DOI":"10.1093\/bioinformatics\/18.11.1454","article-title":"Nonparametric methods for identifying differentially expressed genes in microarray data","volume":"18","author":"Troyanskaya","year":"2002","journal-title":"Bioinformatics"},{"key":"2023012508052910700_B42","doi-asserted-by":"crossref","first-page":"5116","DOI":"10.1073\/pnas.091062498","article-title":"Significance analysis of microarrays applied to the ionizing radiation response","volume":"98","author":"Tusher","year":"2001","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"2023012508052910700_B43","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.1056\/NEJMoa021967","article-title":"A gene-expression signature as a predictor of survival in breast cancer","volume":"347","author":"van de Vijver","year":"2002","journal-title":"N. Engl. J. Med."},{"issue":"Suppl. 1","key":"2023012508052910700_B44","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/S0959-8049(00)00405-6","article-title":"First-line, single-agent herceptin(r) (trastuzumab) in metastatic breast cancer. a preliminary report","volume":"37","author":"Vogel","year":"2001","journal-title":"Eur. J. Cancer"},{"key":"2023012508052910700_B45","doi-asserted-by":"crossref","first-page":"R65","DOI":"10.1186\/bcr2124","article-title":"Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures","volume":"10","author":"Wirapati","year":"2008","journal-title":"Breast Cancer Res."},{"key":"2023012508052910700_B46","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1038\/nmeth0409-239","article-title":"The UCSC cancer genomics browser","volume":"6","author":"Zhu","year":"2009","journal-title":"Nat. Methods"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/26\/12\/i237\/48858283\/bioinformatics_26_12_i237.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/26\/12\/i237\/48858283\/bioinformatics_26_12_i237.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T18:28:53Z","timestamp":1740162533000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/26\/12\/i237\/282591"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2010,6,1]]},"references-count":46,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2010,6,15]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btq182","relation":{},"ISSN":["1367-4811","1367-4803"],"issn-type":[{"value":"1367-4811","type":"electronic"},{"value":"1367-4803","type":"print"}],"subject":[],"published-other":{"date-parts":[[2010,6,15]]},"published":{"date-parts":[[2010,6,1]]}}}