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In previous studies, the differentially expressed genes were detected across patients in one cancer type. These studies ignored the role of mRNA expression changes in driving tumorigenic mechanisms that are either universal or specific in different tumor types. To address the problem, we introduce two network-based multi-task learning frameworks, NetML and NetSML, to discover common differentially expressed genes shared across different cancer types as well as differentially expressed genes specific to each cancer type. The proposed frameworks consider the common latent gene co-expression modules and gene\u2013sample biclusters underlying the multiple cancer datasets to learn the knowledge crossing different tumor types.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Large-scale experiments on simulations and real cancer high-throughput datasets validate that the proposed network-based multi-task learning frameworks perform better sample classification compared with the models without the knowledge sharing across different cancer types. The common and cancer-specific molecular signatures detected by multi-task learning frameworks on The Cancer Genome Atlas ovarian, breast and prostate cancer datasets are correlated with the known marker genes and enriched in cancer-relevant Kyoto Encyclopedia of Genes and Genome pathways and gene ontology terms.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>Source code is available at: https:\/\/github.com\/compbiolabucf\/NetML.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btz809","type":"journal-article","created":{"date-parts":[[2019,10,30]],"date-time":"2019-10-30T20:12:10Z","timestamp":1572466330000},"page":"1814-1822","source":"Crossref","is-referenced-by-count":23,"title":["Network-based multi-task learning models for biomarker selection and cancer outcome prediction"],"prefix":"10.1093","volume":"36","author":[{"given":"Zhibo","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Central Florida , Orlando, FL 32816, USA"},{"name":"Genomics and Bioinformatics Cluster, University of Central Florida , Orlando, FL 32816, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhezhi","family":"He","sequence":"additional","affiliation":[{"name":"School of Electrical, Computer and Energy Engineering, Arizona State University , Tempe, AZ 85287, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Milan","family":"Shah","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Duke University , Durham, NC, 27708, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Teng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Mathematics, University of Central Florida , Orlando, FL 32816, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deliang","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Electrical, Computer and Energy Engineering, Arizona State University , Tempe, AZ 85287, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3605-9373","authenticated-orcid":false,"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Central Florida , Orlando, FL 32816, USA"},{"name":"Genomics and Bioinformatics Cluster, University of Central Florida , Orlando, FL 32816, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2019,11,5]]},"reference":[{"key":"2023061002090369300_btz809-B1","doi-asserted-by":"crossref","first-page":"e78716","DOI":"10.1371\/journal.pone.0078716","article-title":"The FBI1\/Akirin2 target gene, BCAM, acts as a suppressive oncogene","volume":"8","author":"Akiyama","year":"2013","journal-title":"PLoS One"},{"key":"2023061002090369300_btz809-B2","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1158\/0008-5472.CAN-07-2120","article-title":"Integrating global gene expression and radiation survival parameters across the 60 cell lines of the National Cancer Institute Anticancer Drug Screen","volume":"68","author":"Amundson","year":"2008","journal-title":"Cancer Res"},{"key":"2023061002090369300_btz809-B3","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1093\/bioinformatics\/17.6.509","article-title":"A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes","volume":"17","author":"Baldi","year":"2001","journal-title":"Bioinformatics"},{"key":"2023061002090369300_btz809-B4","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.febslet.2004.07.055","article-title":"Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments","volume":"573","author":"Breitling","year":"2004","journal-title":"FEBS Lett"},{"key":"2023061002090369300_btz809-B5","doi-asserted-by":"crossref","first-page":"33398","DOI":"10.1038\/srep33398","article-title":"An integrative and comparative study of pan-cancer transcriptomes reveals distinct cancer common and specific signatures","volume":"6","author":"Cao","year":"2016","journal-title":"Sci. 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