{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T15:38:30Z","timestamp":1764603510650,"version":"3.37.3"},"reference-count":12,"publisher":"Oxford University Press (OUP)","issue":"23","license":[{"start":{"date-parts":[[2018,6,27]],"date-time":"2018-06-27T00:00:00Z","timestamp":1530057600000},"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\/100001641","name":"GRF","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100001641","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Research Grants Council"},{"name":"RGC"},{"name":"Hong Kong SAR","award":["14102315","14113514","14133016","14100415","14116014","476113"],"award-info":[{"award-number":["14102315","14113514","14133016","14100415","14116014","476113"]}]},{"name":"Focused Innovations Scheme B","award":["1907307"],"award-info":[{"award-number":["1907307"]}]},{"name":"RGC Collaborative Research Fund","award":["C6015-14G","C6002-17GF"],"award-info":[{"award-number":["C6015-14G","C6002-17GF"]}]},{"name":"Ministry of Science and Technology of China","award":["2014CB964700"],"award-info":[{"award-number":["2014CB964700"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"RGC Joint Research Scheme","award":["N_HKUST606\/17"],"award-info":[{"award-number":["N_HKUST606\/17"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Tissue biopsy is commonly used in cancer diagnosis and molecular studies. However, advanced skills are required for determining cancerous status of biopsies and tissue origin of tumor for cancerous ones. Correct classification is essential for downstream experiment design and result interpretation, especially in molecular cancer studies. Methods for accurate classification of cancerous status and tissue origin for pan-cancer biopsies are thus urgently needed.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We developed a deep learning-based classifier, named GeneCT, for predicting cancerous status and tissue origin of pan-cancer biopsies. GeneCT showed high performance on pan-cancer datasets from various sources and outperformed existing tools. We believe that GeneCT can potentially facilitate cancer diagnosis, tumor origin determination and molecular cancer studies.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>GeneCT is implemented in Perl\/R and supported on GNU\/Linux platforms. Source code, testing data and webserver are freely available at http:\/\/sunlab.cpy.cuhk.edu.hk\/GeneCT\/.<\/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\/bty524","type":"journal-article","created":{"date-parts":[[2018,6,26]],"date-time":"2018-06-26T19:22:35Z","timestamp":1530040955000},"page":"4129-4130","source":"Crossref","is-referenced-by-count":18,"title":["GeneCT: a generalizable cancerous status and tissue origin classifier for pan-cancer biopsies"],"prefix":"10.1093","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9883-1616","authenticated-orcid":false,"given":"Kun","family":"Sun","sequence":"first","affiliation":[{"name":"Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, SAR, China"},{"name":"Department of Chemical Pathology, The Chinese University of Hong Kong, Hong Kong, SAR, China"}]},{"given":"Jiguang","family":"Wang","sequence":"additional","affiliation":[{"name":"Divison of Life Science and Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong, SAR, China"}]},{"given":"Huating","family":"Wang","sequence":"additional","affiliation":[{"name":"Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, SAR, China"},{"name":"Department of Orthopaedics and Traumatology, The Chinese University of Hong Kong, Hong Kong, SAR, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5547-9501","authenticated-orcid":false,"given":"Hao","family":"Sun","sequence":"additional","affiliation":[{"name":"Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, SAR, China"},{"name":"Department of Chemical Pathology, The Chinese University of Hong Kong, Hong Kong, SAR, China"}]}],"member":"286","published-online":{"date-parts":[[2018,6,27]]},"reference":[{"key":"2023012712294891600_bty524-B1","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"key":"2023012712294891600_bty524-B2","doi-asserted-by":"crossref","first-page":"S3.","DOI":"10.1186\/1471-2164-16-S11-S3","article-title":"Evaluation of data discretization methods to derive platform independent isoform expression signatures for multi-class tumor subtyping","volume":"16","author":"Jung","year":"2015","journal-title":"BMC Genomics"},{"key":"2023012712294891600_bty524-B3","doi-asserted-by":"crossref","first-page":"3685","DOI":"10.1093\/bioinformatics\/btx531","article-title":"An introduction to deep learning on biological sequence data: examples and solutions","volume":"33","author":"Jurtz","year":"2017","journal-title":"Bioinformatics"},{"key":"2023012712294891600_bty524-B4","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","article-title":"Identifying medical diagnoses and treatable diseases by image-based deep learning","volume":"172","author":"Kermany","year":"2018","journal-title":"Cell"},{"key":"2023012712294891600_bty524-B5","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1186\/s12864-017-3906-0","article-title":"A comprehensive genomic pan-cancer classification using The Cancer Genome Atlas gene expression data","volume":"18","author":"Li","year":"2017","journal-title":"BMC Genomics"},{"key":"2023012712294891600_bty524-B6","doi-asserted-by":"crossref","first-page":"e64","DOI":"10.1093\/nar\/gku121","article-title":"Isoform-level gene signature improves prognostic stratification and accurately classifies glioblastoma subtypes","volume":"42","author":"Pal","year":"2014","journal-title":"Nucleic Acids Res"},{"key":"2023012712294891600_bty524-B7","doi-asserted-by":"crossref","first-page":"13413","DOI":"10.1038\/srep13413","article-title":"Large-scale RNA-seq transcriptome analysis of 4043 cancers and 548 normal tissue controls across 12 TCGA cancer types","volume":"5","author":"Peng","year":"2015","journal-title":"Sci. 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