{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:34:04Z","timestamp":1772138044896,"version":"3.50.1"},"reference-count":55,"publisher":"Oxford University Press (OUP)","issue":"19","license":[{"start":{"date-parts":[[2021,4,27]],"date-time":"2021-04-27T00:00:00Z","timestamp":1619481600000},"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\/100000163","name":"ARC","doi-asserted-by":"publisher","award":["200100200"],"award-info":[{"award-number":["200100200"]}],"id":[{"id":"10.13039\/100000163","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Australian Research Council Discovery","award":["DP170101306"],"award-info":[{"award-number":["DP170101306"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,10,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Unravelling cancer driver genes is important in cancer research. Although computational methods have been developed to identify cancer drivers, most of them detect cancer drivers at population level. However, two patients who have the same cancer type and receive the same treatment may have different outcomes because each patient has a different genome and their disease might be driven by different driver genes. Therefore new methods are being developed for discovering cancer drivers at individual level, but existing personalized methods only focus on coding drivers while microRNAs (miRNAs) have been shown to drive cancer progression as well. Thus, novel methods are required to discover both coding and miRNA cancer drivers at individual level.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We propose the novel method, pDriver, to discover personalized cancer drivers. pDriver includes two stages: (i) constructing gene networks for each cancer patient and (ii) discovering cancer drivers for each patient based on the constructed gene networks. To demonstrate the effectiveness of pDriver, we have applied it to five TCGA cancer datasets and compared it with the state-of-the-art methods. The result indicates that pDriver is more effective than other methods. Furthermore, pDriver can also detect miRNA cancer drivers and most of them have been confirmed to be associated with cancer by literature. We further analyze the predicted personalized drivers for breast cancer patients and the result shows that they are significantly enriched in many GO processes and KEGG pathways involved in breast cancer.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>pDriver is available at https:\/\/github.com\/pvvhoang\/pDriver.<\/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\/btab262","type":"journal-article","created":{"date-parts":[[2021,4,22]],"date-time":"2021-04-22T20:04:18Z","timestamp":1619121858000},"page":"3285-3292","source":"Crossref","is-referenced-by-count":13,"title":["<i>pDriver<\/i>\n                    : a novel method for unravelling personalized coding and miRNA cancer drivers"],"prefix":"10.1093","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8884-3584","authenticated-orcid":false,"given":"Vu V H","family":"Pham","sequence":"first","affiliation":[{"name":"UniSA STEM, University of South Australia , Mawson Lakes, SA 5095, Australia"}]},{"given":"Lin","family":"Liu","sequence":"additional","affiliation":[{"name":"UniSA STEM, University of South Australia , Mawson Lakes, SA 5095, Australia"}]},{"given":"Cameron P","family":"Bracken","sequence":"additional","affiliation":[{"name":"Centre for Cancer Biology, An alliance of SA Pathology and University of South Australia , Adelaide, SA 5000, Australia"},{"name":"Department of Medicine, The University of Adelaide , Adelaide, SA 5005, Australia"}]},{"given":"Thin","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Applied Artificial Intelligence Institute, Deakin University , Burwood, VIC 3125, Australia"}]},{"given":"Gregory J","family":"Goodall","sequence":"additional","affiliation":[{"name":"Centre for Cancer Biology, An alliance of SA Pathology and University of South Australia , Adelaide, SA 5000, Australia"},{"name":"Department of Medicine, The University of Adelaide , Adelaide, SA 5005, Australia"}]},{"given":"Jiuyong","family":"Li","sequence":"additional","affiliation":[{"name":"UniSA STEM, University of South Australia , Mawson Lakes, SA 5095, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9732-4313","authenticated-orcid":false,"given":"Thuc D","family":"Le","sequence":"additional","affiliation":[{"name":"UniSA STEM, University of South Australia , Mawson Lakes, SA 5095, Australia"}]}],"member":"286","published-online":{"date-parts":[[2021,4,27]]},"reference":[{"key":"2023051701221856700_btab262-B1","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":"2023051701221856700_btab262-B2","doi-asserted-by":"crossref","first-page":"2850","DOI":"10.1158\/1078-0432.CCR-08-3131","article-title":"MicroRNA expression ratio is predictive of head and neck squamous cell carcinoma","volume":"15","author":"Avissar","year":"2009","journal-title":"Clin. 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