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In addition, many databases have amassed information about pathways and gene \u201csignatures\u201d\u2014patterns of gene expression associated with specific cellular and phenotypic contexts. An important current challenge in systems biology is to leverage such knowledge about gene coordination to maximize the predictive power and generalization of models applied to high-throughput datasets. However, few such integrative approaches exist that also provide interpretable results quantifying the importance of individual genes and pathways to model accuracy. We introduce AKLIMATE, a first kernel-based stacked learner that seamlessly incorporates multi-omics feature data with prior information in the form of pathways for either regression or classification tasks. AKLIMATE uses a novel multiple-kernel learning framework where individual kernels capture the prediction propensities recorded in random forests, each built from a specific pathway gene set that integrates all omics data for its member genes. AKLIMATE has comparable or improved performance relative to state-of-the-art methods on diverse phenotype learning tasks, including predicting microsatellite instability in endometrial and colorectal cancer, survival in breast cancer, and cell line response to gene knockdowns. We show how AKLIMATE is able to connect feature data across data platforms through their common pathways to identify examples of several known and novel contributors of cancer and synthetic lethality.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1008878","type":"journal-article","created":{"date-parts":[[2021,4,16]],"date-time":"2021-04-16T14:46:47Z","timestamp":1618584407000},"page":"e1008878","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":12,"title":["Accurate cancer phenotype prediction with AKLIMATE, a stacked kernel learner integrating multimodal genomic data and pathway knowledge"],"prefix":"10.1371","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1962-0977","authenticated-orcid":true,"given":"Vladislav","family":"Uzunangelov","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6012-7001","authenticated-orcid":true,"given":"Christopher K.","family":"Wong","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2171-565X","authenticated-orcid":true,"given":"Joshua M.","family":"Stuart","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2021,4,16]]},"reference":[{"issue":"2","key":"pcbi.1008878.ref001","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/j.cell.2018.03.022","article-title":"Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer","volume":"173","author":"KA Hoadley","year":"2018","journal-title":"Cell"},{"issue":"Database issue","key":"pcbi.1008878.ref002","doi-asserted-by":"crossref","first-page":"D685","DOI":"10.1093\/nar\/gkq1039","article-title":"Pathway Commons, a web resource for biological pathway data","volume":"39","author":"EG Cerami","year":"2011","journal-title":"Nucleic Acids Research"},{"issue":"12","key":"pcbi.1008878.ref003","doi-asserted-by":"crossref","first-page":"1739","DOI":"10.1093\/bioinformatics\/btr260","article-title":"Molecular signatures database (MSigDB) 3.0","volume":"27","author":"A Liberzon","year":"2011","journal-title":"Bioinformatics"},{"issue":"Database issue","key":"pcbi.1008878.ref004","doi-asserted-by":"crossref","first-page":"D1060","DOI":"10.1093\/nar\/gkr901","article-title":"GeneSigDB: a manually curated database and resource for analysis of gene expression signatures","volume":"40","author":"AC Culhane","year":"2012","journal-title":"Nucleic Acids Research"},{"issue":"W1","key":"pcbi.1008878.ref005","doi-asserted-by":"crossref","first-page":"W115","DOI":"10.1093\/nar\/gkt533","article-title":"GeneMANIA Prediction Server 2013 Update","volume":"41","author":"K Zuberi","year":"2013","journal-title":"Nucleic Acids Research"},{"issue":"4","key":"pcbi.1008878.ref006","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1016\/j.cels.2015.10.001","article-title":"NDEx, the Network Data Exchange","volume":"1","author":"D Pratt","year":"2015","journal-title":"Cell Systems"},{"issue":"16","key":"pcbi.1008878.ref007","doi-asserted-by":"crossref","first-page":"0","DOI":"10.1186\/s12859-016-1311-3","article-title":"Integrating gene set analysis and nonlinear predictive modeling of disease phenotypes using a Bayesian multitask formulation","volume":"17","author":"M G\u00f6nen","year":"2016","journal-title":"BMC Bioinformatics"},{"issue":"4","key":"pcbi.1008878.ref008","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1016\/j.cell.2014.06.049","article-title":"Multiplatform Analysis of 12 Cancer Types Reveals Molecular Classification within and across Tissues of Origin","volume":"158","author":"KA Hoadley","year":"2014","journal-title":"Cell"},{"issue":"12","key":"pcbi.1008878.ref009","doi-asserted-by":"crossref","first-page":"1202","DOI":"10.1038\/nbt.2877","article-title":"A community effort to assess and improve drug sensitivity prediction algorithms","volume":"32","author":"JC Costello","year":"2014","journal-title":"Nature Biotechnology"},{"issue":"Database issue","key":"pcbi.1008878.ref010","doi-asserted-by":"crossref","first-page":"D674","DOI":"10.1093\/nar\/gkn653","article-title":"PID: the Pathway Interaction Database","volume":"37","author":"CF Schaefer","year":"2009","journal-title":"Nucleic Acids Research"},{"key":"pcbi.1008878.ref011","unstructured":"Tomioka R, Suzuki T. 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