{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T07:02:00Z","timestamp":1776409320362,"version":"3.51.2"},"reference-count":38,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2021,8,20]],"date-time":"2021-08-20T00:00:00Z","timestamp":1629417600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["NSF-III1755761"],"award-info":[{"award-number":["NSF-III1755761"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["2R01GM113952-06"],"award-info":[{"award-number":["2R01GM113952-06"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["DK097771"],"award-info":[{"award-number":["DK097771"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,12,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Accurate disease phenotype prediction plays an important role in the treatment of heterogeneous diseases like cancer in the era of precision medicine. With the advent of high throughput technologies, more comprehensive multi-omics data is now available that can effectively link the genotype to phenotype. However, the interactive relation of multi-omics datasets makes it particularly challenging to incorporate different biological layers to discover the coherent biological signatures and predict phenotypic outcomes. In this study, we introduce omicsGAN, a generative adversarial network model to integrate two omics data and their interaction network. The model captures information from the interaction network as well as the two omics datasets and fuse them to generate synthetic data with better predictive signals.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Large-scale experiments on The Cancer Genome Atlas breast cancer, lung cancer and ovarian cancer datasets validate that (i) the model can effectively integrate two omics data (e.g. mRNA and microRNA expression data) and their interaction network (e.g. microRNA-mRNA interaction network). The synthetic omics data generated by the proposed model has a better performance on cancer outcome classification and patients survival prediction compared to original omics datasets. (ii) The integrity of the interaction network plays a vital role in the generation of synthetic data with higher predictive quality. Using a random interaction network does not allow the framework to learn meaningful information from the omics datasets; therefore, results in synthetic data with weaker predictive signals.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>Source code is available at: https:\/\/github.com\/CompbioLabUCF\/omicsGAN.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab608","type":"journal-article","created":{"date-parts":[[2021,8,18]],"date-time":"2021-08-18T15:18:12Z","timestamp":1629299892000},"page":"179-186","source":"Crossref","is-referenced-by-count":79,"title":["Multi-omics data integration by generative adversarial network"],"prefix":"10.1093","volume":"38","author":[{"given":"Khandakar Tanvir","family":"Ahmed","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"}]},{"given":"Jiao","family":"Sun","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"}]},{"given":"Sze","family":"Cheng","sequence":"additional","affiliation":[{"name":"Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota Twin Cities , Minneapolis, MN 55455, USA"}]},{"given":"Jeongsik","family":"Yong","sequence":"additional","affiliation":[{"name":"Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota Twin Cities , Minneapolis, MN 55455, USA"}]},{"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"}]}],"member":"286","published-online":{"date-parts":[[2021,8,20]]},"reference":[{"key":"2023033101030441100_","doi-asserted-by":"crossref","first-page":"e05005","DOI":"10.7554\/eLife.05005","article-title":"Predicting effective microRNA target sites in mammalian mRNAs","volume":"4","author":"Agarwal","year":"2015","journal-title":"elife"},{"key":"2023033101030441100_","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12920-020-00829-3","article-title":"Network-based drug sensitivity prediction","volume":"13","author":"Ahmed","year":"2020","journal-title":"BMC Med. 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