{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T11:33:58Z","timestamp":1787312038839,"version":"build-2736575974"},"reference-count":84,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100012131","name":"Liaoning Science and Technology Department, China, through the project titled \u201cResearch on the Mechanism of VH4-34 Encoding Human IgM Anti-Cancer Immune Micro-Environment,\u201d","doi-asserted-by":"publisher","award":["2020-BS-099"],"award-info":[{"award-number":["2020-BS-099"]}],"id":[{"id":"10.13039\/501100012131","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012131","name":"Liaoning Science and Technology Department, China, through the project titled \u201cResearch on the Mechanism of VH4-34 Encoding Human IgM Anti-Cancer Immune Micro-Environment,\u201d","doi-asserted-by":"publisher","award":["2020.05.01-2023.04.30"],"award-info":[{"award-number":["2020.05.01-2023.04.30"]}],"id":[{"id":"10.13039\/501100012131","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/access.2021.3107498","type":"journal-article","created":{"date-parts":[[2021,8,24]],"date-time":"2021-08-24T16:13:43Z","timestamp":1629821623000},"page":"120464-120480","source":"Crossref","is-referenced-by-count":9,"title":["A Machine Learning-Aided Framework to Predict Outcomes of Anti-PD-1 Therapy for Patients With Gynecological Cancer on Incomplete Post-Marketing Surveillance Dataset"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0934-7249","authenticated-orcid":false,"given":"Xiaomei","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3327-8108","authenticated-orcid":false,"given":"Zhifeng","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruizhe","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiuqin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenhua","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref73","author":"raschka","year":"2015","journal-title":"Machine Learning in Python"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1007\/BF00994018"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.2307\/2347628"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1958.tb00292.x"},{"key":"ref76","first-page":"181","article-title":"Practical feature subset selection for machine learning","author":"hall","year":"1998","journal-title":"Proc 21st Australas Comput Sci Conf (ACSC)"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-57868-4_57"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2016.09.038"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3057931"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.06.022"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-019-8208-z"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-012-0487-8"},{"key":"ref79","article-title":"Hyperparameter search in machine learning","author":"claesen","year":"2015","journal-title":"arXiv 1502 02127"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.3390\/agronomy11081500"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2696121"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-12-815739-8.00014-6"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2010.05.002"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bth267"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.12.016"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.17148\/IARJSET.2015.2305"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2932786"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-006-6226-1"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"ref61","first-page":"3146","article-title":"LightGBM: A highly efficient gradient boosting decision tree","volume":"30","author":"ke","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-36365-9_25"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.3390\/app9153156"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-020-00369-8"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2696365"},{"key":"ref65","first-page":"1551","article-title":"Cost efficient gradient boosting","author":"peter","year":"2017","journal-title":"Proc NIPS"},{"key":"ref66","doi-asserted-by":"crossref","first-page":"349","DOI":"10.4310\/SII.2009.v2.n3.a8","article-title":"Multi-class AdaBoost","volume":"2","author":"zhu","year":"2009","journal-title":"Statist Interface"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1088\/978-0-7503-2404-5ch8"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/T-C.1975.224297"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1504\/IJAPR.2016.079050"},{"key":"ref69","first-page":"41","article-title":"An empirical study of the naive Bayes classifier","volume":"3","author":"rish","year":"2001","journal-title":"Proc Workshop Empirical Methods Artif Intell (IJCAI)"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1186\/s12943-018-0864-3"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1080\/14656566.2018.1518433"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.jaad.2020.04.069"},{"key":"ref22","article-title":"Anti-PD-1 based immunotherapy in melanoma: Application of machine learning to predict survival and elucidate complex relationships","author":"naik","year":"2018"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1136\/esmoopen-2020-000910"},{"key":"ref24","article-title":"A machine learning algorithm to predict hyperglycemic cases induced by PD-1\/PD-L1 inhibitors in the real world","author":"yang","year":"2020","journal-title":"SquareTrade Research"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1177\/1533033819875766"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejca.2021.05.019"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1158\/1078-0432.CCR-20-2415"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btr597"},{"key":"ref51","first-page":"5689","article-title":"Gain: Missing data imputation using generative adversarial nets","author":"yoon","year":"2018","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781316576533"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1198\/jasa.2004.s339"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1007\/s11749-016-0481-7"},{"key":"ref55","first-page":"500","article-title":"A fast decision tree learning algorithm","volume":"6","author":"su","year":"2006","journal-title":"Proc AAAI"},{"key":"ref54","author":"zheng","year":"2018","journal-title":"Feature Engineering for Machine Learning Principles and Techniques for Data Scientists"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1016\/B978-1-55860-247-2.50037-1"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.5120\/ijca2017915495"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1186\/s40425-017-0300-z"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1093\/annonc\/mdv383"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1613\/jair.953"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMra1703481"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1136\/jitc-2020-SITC2020.0186"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.ygyno.2015.01.514"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.critrevonc.2020.102973"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.3390\/en11040949"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.3390\/ijms21145034"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2021.106039"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1111\/1759-7714.13078"},{"key":"ref84","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"pedregosa","year":"2011","journal-title":"J Mach Learn Res"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejca.2020.09.015"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1109\/IC3INA48034.2019.8949568"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1158\/2159-8290.CD-20-0419"},{"key":"ref80","first-page":"1","article-title":"Random search for hyper-parameter optimization","volume":"13","author":"bergstra","year":"2012","journal-title":"J Mach Learn Res"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1200690"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1158\/2159-8290.CD-20-0596"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/s12325-018-0683-3"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1097\/IGC.0000000000000999"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.7150\/jca.22532"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/s11864-018-0593-2"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1002\/sim.4067"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/s00428-020-02751-6"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.21037\/atm-20-3623"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1177\/1740774508091600"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2012.05.073"},{"key":"ref47","first-page":"211","article-title":"Sparse Bayesian learning and the relevance vector machine","volume":"1","author":"tipping","year":"2001","journal-title":"J Mach Learn Res"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/s40265-019-01167-0"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemolab.2006.01.007"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/s40265-019-01076-2"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/s40265-019-1066-z"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9312710\/09521878.pdf?arnumber=9521878","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T14:57:38Z","timestamp":1639753058000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9521878\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":84,"URL":"https:\/\/doi.org\/10.1109\/access.2021.3107498","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}