{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T12:01:03Z","timestamp":1780488063831,"version":"3.54.1"},"reference-count":46,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2022,12,14]],"date-time":"2022-12-14T00:00:00Z","timestamp":1670976000000},"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\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61902126"],"award-info":[{"award-number":["61902126"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Science and Technology Program","award":["20511100600"],"award-info":[{"award-number":["20511100600"]}]},{"name":"Shanghai Science and Technology Program","award":["21511100800"],"award-info":[{"award-number":["21511100800"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,1,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The development of targeted drugs allows precision medicine in cancer treatment and optimal targeted therapies. Accurate identification of cancer druggable genes helps strengthen the understanding of targeted cancer therapy and promotes precise cancer treatment. However, rare cancer-druggable genes have been found due to the multi-omics data\u2019s diversity and complexity. This study proposes deep forest for cancer druggable genes discovery (DF-CAGE), a novel machine learning-based method for cancer-druggable gene discovery. DF-CAGE integrated the somatic mutations, copy number variants, DNA methylation and RNA-Seq data across \u02dc10 000 TCGA profiles to identify the landscape of the cancer-druggable genes. We found that DF-CAGE discovers the commonalities of currently known cancer-druggable genes from the perspective of multi-omics data and achieved excellent performance on OncoKB, Target and Drugbank data sets. Among the \u02dc20 000 protein-coding genes, DF-CAGE pinpointed 465 potential cancer-druggable genes. We found that the candidate cancer druggable genes (CDG) are clinically meaningful and divided the CDG into known, reliable and potential gene sets. Finally, we analyzed the omics data\u2019s contribution to identifying druggable genes. We found that DF-CAGE reports druggable genes mainly based on the copy number variations (CNVs) data, the gene rearrangements and the mutation rates in the population. These findings may enlighten the future study and development of new drugs.<\/jats:p>","DOI":"10.1093\/bib\/bbac528","type":"journal-article","created":{"date-parts":[[2022,11,9]],"date-time":"2022-11-09T17:50:04Z","timestamp":1668016204000},"source":"Crossref","is-referenced-by-count":15,"title":["From multi-omics data to the cancer druggable gene discovery: a novel machine learning-based approach"],"prefix":"10.1093","volume":"24","author":[{"given":"Hai","family":"Yang","sequence":"first","affiliation":[{"name":"East China University of Science and Technology Department of Computer Science and Engineering, , 200237 Shanghai, PR China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lipeng","family":"Gan","sequence":"additional","affiliation":[{"name":"East China University of Science and Technology Department of Computer Science and Engineering, , 200237 Shanghai, PR 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