{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T18:05:19Z","timestamp":1775325919332,"version":"3.50.1"},"reference-count":50,"publisher":"Oxford University Press (OUP)","issue":"17","license":[{"start":{"date-parts":[[2018,9,9]],"date-time":"2018-09-09T00:00:00Z","timestamp":1536451200000},"content-version":"vor","delay-in-days":8,"URL":"https:\/\/academic.oup.com\/journals\/pages\/about_us\/legal\/notices"}],"funder":[{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["R01 AG049371"],"award-info":[{"award-number":["R01 AG049371"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["IIS 1302675"],"award-info":[{"award-number":["IIS 1302675"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["IIS 1344152"],"award-info":[{"award-number":["IIS 1344152"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["DBI 1356628"],"award-info":[{"award-number":["DBI 1356628"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["IIS 1619308"],"award-info":[{"award-number":["IIS 1619308"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["IIS 1633753"],"award-info":[{"award-number":["IIS 1633753"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>The rapid progress of gene expression profiling has facilitated the prosperity of recent biological studies in various fields, where gene expression data characterizes various cell conditions and regulatory mechanisms under different experimental circumstances. Despite the widespread application of gene expression profiling and advances in high-throughput technologies, profiling in genome-wide level is still expensive and difficult. Previous studies found that high correlation exists in the expression pattern of different genes, such that a small subset of genes can be informative to approximately describe the entire transcriptome. In the Library of Integrated Network-based Cell-Signature program, a set of \u223c1000 landmark genes have been identified that contain \u223c80% information of the whole genome and can be used to predict the expression of remaining genes. For a cost-effective profiling strategy, traditional methods measure the profiles of landmark genes and then infer the expression of other target genes via linear models. However, linear models do not have the capacity to capture the non-linear associations in gene regulatory networks.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>As a flexible model with high representative power, deep learning models provide an alternate to interpret the complex relation among genes. In this paper, we propose a deep learning architecture for the inference of target gene expression profiles. We construct a novel conditional generative adversarial network by incorporating both the adversarial and \u21131-norm loss terms in our model. Unlike the smooth and blurry predictions resulted by mean squared error objective, the coupled adversarial and \u21131-norm loss function leads to more accurate and sharp predictions. We validate our method under two different settings and find consistent and significant improvements over all the comparing methods.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/bty563","type":"journal-article","created":{"date-parts":[[2018,7,7]],"date-time":"2018-07-07T05:42:12Z","timestamp":1530942132000},"page":"i603-i611","source":"Crossref","is-referenced-by-count":55,"title":["Conditional generative adversarial network for gene expression inference"],"prefix":"10.1093","volume":"34","author":[{"given":"Xiaoqian","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA"}]},{"given":"Kamran","family":"Ghasedi Dizaji","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA"}]},{"given":"Heng","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA"}]}],"member":"286","published-online":{"date-parts":[[2018,9,8]]},"reference":[{"key":"2023061402421929800_bty563-B1","doi-asserted-by":"crossref","first-page":"e0130140","DOI":"10.1371\/journal.pone.0130140","article-title":"On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation","volume":"10","author":"Bach","year":"2015","journal-title":"PLoS One"},{"key":"2023061402421929800_bty563-B2","article-title":"Chemgan challenge for drug discovery: can ai reproduce natural chemical diversity?","author":"Benhenda","year":"2017"},{"key":"2023061402421929800_bty563-B3","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1038\/ng.3225","article-title":"Stromal gene expression defines poor-prognosis subtypes in colorectal cancer","volume":"47","author":"Calon","year":"2015","journal-title":"Nat. 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