{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T08:07:17Z","timestamp":1769846837571,"version":"3.49.0"},"reference-count":45,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2021,5,10]],"date-time":"2021-05-10T00:00:00Z","timestamp":1620604800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Ningxia Key Research and Development Program","award":["2019BFG02002"],"award-info":[{"award-number":["2019BFG02002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,11,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Although chemotherapy is the first-line treatment for ovarian cancer (OCa) patients, chemoresistance (CR) decreases their progression-free survival. This paper investigates the genetic interaction (GI) related to OCa-CR. To decrease the complexity of establishing gene networks, individual signature genes related to OCa-CR are identified using a gradient boosting decision tree algorithm. Additionally, the genetic interaction coefficient (GIC) is proposed to measure the correlation of two signature genes quantitatively and explain their joint influence on OCa-CR. Gene pair that possesses high GIC is identified as signature pair. A total of 24 signature gene pairs are selected that include 10 individual signature genes and the influence of signature gene pairs on OCa-CR is explored. Finally, a signature gene pair-based prediction of OCa-CR is identified. The area under curve (AUC) is a widely used performance measure for machine learning prediction. The AUC of signature gene pair reaches 0.9658, whereas the AUC of individual signature gene-based prediction is 0.6823 only. The identified signature gene pairs not only build an efficient GI network of OCa-CR but also provide an interesting way for OCa-CR prediction. This improvement shows that our proposed method is a useful tool to investigate GI related to OCa-CR.<\/jats:p>","DOI":"10.1093\/bib\/bbab100","type":"journal-article","created":{"date-parts":[[2021,3,11]],"date-time":"2021-03-11T12:11:02Z","timestamp":1615464662000},"source":"Crossref","is-referenced-by-count":10,"title":["Integration and interplay of machine learning and bioinformatics approach to identify genetic interaction related to ovarian cancer chemoresistance"],"prefix":"10.1093","volume":"22","author":[{"given":"Kexin","family":"Chen","sequence":"first","affiliation":[{"name":"School of Electronics Engineering and Computer Science, Peking University, 100871, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haoming","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Duke University, 27708, Durham, United 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