{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T12:31:33Z","timestamp":1770813093681,"version":"3.50.1"},"reference-count":51,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T00:00:00Z","timestamp":1758412800000},"content-version":"vor","delay-in-days":21,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000092","name":"National Library of Medicine","doi-asserted-by":"publisher","award":["R01LM012011 to XL"],"award-info":[{"award-number":["R01LM012011 to XL"]}],"id":[{"id":"10.13039\/100000092","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000050","name":"National Heart, Lung and Blood Institute","doi-asserted-by":"crossref","award":["[K01HL161538 and R03HL168984 to JL] and [R01HL164835 to GFC]"],"award-info":[{"award-number":["[K01HL161538 and R03HL168984 to JL] and [R01HL164835 to GFC]"]}],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,31]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Cancer is mainly caused by a relatively small portion of somatic genome alterations (SGAs), called cancer drivers. Despite success in identifying a good number of cancer drivers, many more remain to be discovered to explain various cancers. Moreover, limited tools are available to identify potential interactions among cancer drivers for a better understanding of oncogenesis. To tackle these challenges, we have developed a novel approach called individualized Bayesian inference using a decision tree (IBI-DT). IBI-DT recognizes the genetic heterogeneity among cancer patients, where different individuals or patient subgroups of distinct genomic makeup may have different drivers. IBI-DT works by constructing smaller subgroups with similar genetic makeup (i.e. patient-like-me subgroups) using a decision tree structure and analyzing multiple trees to identify the SGAs that play a significant role in regulating downstream gene expression patterns at the subgroup and individual levels. This is distinct from population-based approaches, which tend to evaluate the influence of an SGA for the entire population, thereby likely missing low-frequency SGAs that may well explain a small subgroup of cancer patients. Also importantly, IBI-DT can efficiently identify cancer drivers that may have functional interactions. We applied IBI-DT to identify cancer drivers regulating the downstream differential gene expression in cancer patients and compared it to the standard, population-based method of expression quantitative trait loci analysis. Our results show that IBI-DT performs well in identifying both important cancer drivers, especially the low-frequency drivers, and their interactions, allowing for a better understanding of the cancer signaling pathways.<\/jats:p>","DOI":"10.1093\/bib\/bbaf463","type":"journal-article","created":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T11:50:48Z","timestamp":1758369048000},"source":"Crossref","is-referenced-by-count":0,"title":["IBI-DT: a novel approach combining individualized Bayesian inference and decision tree for identifying cancer drivers and their interactions"],"prefix":"10.1093","volume":"26","author":[{"given":"Md Asad","family":"Rahman","sequence":"first","affiliation":[{"name":"Department of Engineering Management and Systems Engineering, Missouri University of Science and Technology , 600 W 14th St, Rolla, MO 65409 ,","place":["United States"]},{"name":"Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, 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