{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T09:02:36Z","timestamp":1770541356768,"version":"3.49.0"},"reference-count":61,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"vor","delay-in-days":9,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62173117"],"award-info":[{"award-number":["62173117"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32000473"],"award-info":[{"award-number":["32000473"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005046","name":"Natural Science Foundation of Heilongjiang Province","doi-asserted-by":"publisher","award":["LH2020C100"],"award-info":[{"award-number":["LH2020C100"]}],"id":[{"id":"10.13039\/501100005046","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Cooperative Scientific Research Project of \u2018Chunhui plan\u2019 for Ministry of Education","award":["HLJ201919"],"award-info":[{"award-number":["HLJ201919"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,11,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Pancreatic cancer is a globally recognized highly aggressive malignancy, posing a significant threat to human health and characterized by pronounced heterogeneity. In recent years, researchers have uncovered that the development and progression of cancer are often attributed to the accumulation of somatic mutations within cells. However, cancer somatic mutation data exhibit characteristics such as high dimensionality and sparsity, which pose new challenges in utilizing these data effectively. In this study, we propagated the discrete somatic mutation data of pancreatic cancer through a network propagation model based on protein\u2013protein interaction networks. This resulted in smoothed somatic mutation profile data that incorporate protein network information. Based on this smoothed mutation profile data, we obtained the activity levels of different metabolic pathways in pancreatic cancer patients. Subsequently, using the activity levels of various metabolic pathways in cancer patients, we employed a deep clustering algorithm to establish biologically and clinically relevant metabolic subtypes of pancreatic cancer. Our study holds scientific significance in classifying pancreatic cancer based on somatic mutation data and may provide a crucial theoretical basis for the diagnosis and immunotherapy of pancreatic cancer patients.<\/jats:p>","DOI":"10.1093\/bib\/bbad430","type":"journal-article","created":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T02:38:01Z","timestamp":1701484681000},"source":"Crossref","is-referenced-by-count":4,"title":["Integrating somatic mutation profiles with structural deep clustering network for metabolic stratification in pancreatic cancer: a comprehensive analysis of prognostic and genomic landscapes"],"prefix":"10.1093","volume":"25","author":[{"given":"Min","family":"Zou","sequence":"first","affiliation":[{"name":"College of Bioinformatics Science and Technology, Harbin Medical University , Harbin 150081 , China"}]},{"given":"Honghao","family":"Li","sequence":"additional","affiliation":[{"name":"College of Bioinformatics Science and Technology, Harbin Medical University , Harbin 150081 , China"}]},{"given":"Dongqing","family":"Su","sequence":"additional","affiliation":[{"name":"College of Bioinformatics Science and Technology, Harbin Medical University , Harbin 150081 , China"}]},{"given":"Yuqiang","family":"Xiong","sequence":"additional","affiliation":[{"name":"College of Bioinformatics Science and Technology, Harbin Medical University , Harbin 150081 , China"}]},{"given":"Haodong","family":"Wei","sequence":"additional","affiliation":[{"name":"College of Bioinformatics Science and Technology, Harbin Medical University , Harbin 150081 , China"}]},{"given":"Shiyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Bioinformatics Science and Technology, Harbin Medical University , Harbin 150081 , China"}]},{"given":"Hongmei","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Bioinformatics Science and Technology, Harbin Medical University , Harbin 150081 , China"}]},{"given":"Tao","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Bioinformatics Science and Technology, Harbin Medical University , Harbin 150081 , China"}]},{"given":"Qilemuge","family":"Xi","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock , College of Life Sciences, , Hohhot 010070 , China"},{"name":"Inner Mongolia University , College of Life Sciences, , Hohhot 010070 , China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6065-7835","authenticated-orcid":false,"given":"Yongchun","family":"Zuo","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock , College of Life Sciences, , Hohhot 010070 , China"},{"name":"Inner Mongolia University , College of Life Sciences, , Hohhot 010070 , China"},{"name":"Digital College , Inner Mongolia Intelligent Union Big Data Academy, . Hohhot 010010, China"},{"name":"Inner Mongolia Wesure Date Technology Co., Ltd , Inner Mongolia Intelligent Union Big Data Academy, . Hohhot 010010, China"},{"name":"Inner Mongolia International Mongolian Hospital , Hohhot 010065 , China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1133-9099","authenticated-orcid":false,"given":"Lei","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Bioinformatics Science and Technology, Harbin Medical University , Harbin 150081 , China"}]}],"member":"286","published-online":{"date-parts":[[2023,12,1]]},"reference":[{"key":"2024011119381898300_ref1","doi-asserted-by":"crossref","first-page":"394","DOI":"10.3322\/caac.21492","article-title":"Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"68","author":"Bray","year":"2018","journal-title":"CA Cancer J Clin"},{"key":"2024011119381898300_ref2","doi-asserted-by":"crossref","first-page":"209","DOI":"10.3322\/caac.21660","article-title":"Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA Cancer J Clin"},{"key":"2024011119381898300_ref3","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.ccell.2017.07.007","article-title":"Integrated genomic characterization of pancreatic ductal adenocarcinoma","volume":"32","author":"Raphael","year":"2017","journal-title":"Cancer Cell"},{"key":"2024011119381898300_ref4","doi-asserted-by":"crossref","first-page":"2139","DOI":"10.1056\/NEJMra1404198","article-title":"Pancreatic adenocarcinoma","volume":"371","author":"Ryan","year":"2014","journal-title":"N Engl J Med"},{"key":"2024011119381898300_ref5","doi-asserted-by":"crossref","first-page":"17","DOI":"10.3322\/caac.21763","article-title":"Cancer statistics, 2023","volume":"73","author":"Siegel","year":"2023","journal-title":"CA Cancer J Clin"},{"key":"2024011119381898300_ref6","doi-asserted-by":"crossref","first-page":"7","DOI":"10.3322\/caac.21708","article-title":"Cancer statistics, 2022","volume":"72","author":"Siegel","year":"2022","journal-title":"CA Cancer J Clin"},{"key":"2024011119381898300_ref7","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1038\/nrclinonc.2009.236","article-title":"Advanced pancreatic carcinoma: current treatment and future challenges","volume":"7","author":"Stathis","year":"2010","journal-title":"Nat Rev Clin Oncol"},{"key":"2024011119381898300_ref8","doi-asserted-by":"crossref","first-page":"896","DOI":"10.1002\/1878-0261.12639","article-title":"Metabolism-associated molecular classification of hepatocellular carcinoma","volume":"14","author":"Yang","year":"2020","journal-title":"Mol Oncol"},{"key":"2024011119381898300_ref9","doi-asserted-by":"crossref","first-page":"e1600200","DOI":"10.1126\/sciadv.1600200","article-title":"Fundamentals of cancer metabolism","volume":"2","author":"DeBerardinis","year":"2016","journal-title":"Sci Adv"},{"key":"2024011119381898300_ref10","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1038\/s41568-020-0273-y","article-title":"Metabolism of immune cells in cancer","volume":"20","author":"Leone","year":"2020","journal-title":"Nat Rev Cancer"},{"key":"2024011119381898300_ref11","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1038\/s41568-021-00378-6","article-title":"Cancer metabolism: looking forward","volume":"21","author":"Mart\u00ednez-Reyes","year":"2021","journal-title":"Nat Rev Cancer"},{"key":"2024011119381898300_ref12","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1038\/ng.3168","article-title":"Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes","volume":"47","author":"Leiserson","year":"2015","journal-title":"Nat Genet"},{"key":"2024011119381898300_ref13","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1038\/nature12213","article-title":"Mutational heterogeneity in cancer and the search for new cancer-associated genes","volume":"499","author":"Lawrence","year":"2013","journal-title":"Nature"},{"key":"2024011119381898300_ref14","doi-asserted-by":"crossref","first-page":"710","DOI":"10.1038\/s41568-023-00602-5","article-title":"Cancers make their own luck: theories of cancer origins","volume":"23","author":"Jassim","year":"2023","journal-title":"Nat Rev Cancer"},{"key":"2024011119381898300_ref15","doi-asserted-by":"crossref","first-page":"2057","DOI":"10.1038\/s41591-023-02482-6","article-title":"Machine learning for genetics-based classification and treatment response prediction in cancer of unknown primary","volume":"29","author":"Moon","year":"2023","journal-title":"Nat Genet"},{"key":"2024011119381898300_ref16","doi-asserted-by":"crossref","first-page":"1492","DOI":"10.1038\/s41416-018-0109-7","article-title":"Cancer subtype identification using somatic mutation data","volume":"118","author":"Kuijjer","year":"2018","journal-title":"Br J Cancer"},{"key":"2024011119381898300_ref17","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1186\/s12918-016-0306-z","article-title":"Classification of breast cancer patients using somatic mutation profiles and machine learning approaches","volume":"10","author":"Vural","year":"2016","journal-title":"BMC Syst Biol"},{"key":"2024011119381898300_ref18","doi-asserted-by":"crossref","first-page":"i484","DOI":"10.1093\/bioinformatics\/bty247","article-title":"Classifying tumors by supervised network propagation","volume":"34","author":"Zhang","year":"2018","journal-title":"Bioinformatics"},{"key":"2024011119381898300_ref19","doi-asserted-by":"crossref","DOI":"10.3389\/fgene.2020.553587","article-title":"Classifying breast cancer molecular subtypes by using deep clustering approach","volume":"11","author":"Rohani","year":"2020","journal-title":"Front Genet"},{"key":"2024011119381898300_ref20","doi-asserted-by":"crossref","first-page":"e0177662","DOI":"10.1371\/journal.pone.0177662","article-title":"Network based stratification of major cancers by integrating somatic mutation and gene expression data","volume":"12","author":"He","year":"2017","journal-title":"PloS One"},{"key":"2024011119381898300_ref21","doi-asserted-by":"crossref","first-page":"S7","DOI":"10.1186\/1471-2164-16-S7-S7","article-title":"Network-based stratification analysis of 13 major cancer types using mutations in panels of cancer genes","volume":"16","author":"Zhong","year":"2015","journal-title":"BMC Genomics"},{"key":"2024011119381898300_ref22","doi-asserted-by":"crossref","first-page":"1108","DOI":"10.1038\/nmeth.2651","article-title":"Network-based stratification of tumor mutations","volume":"10","author":"Hofree","year":"2013","journal-title":"Nat Methods"},{"key":"2024011119381898300_ref23","doi-asserted-by":"crossref","first-page":"1643","DOI":"10.1093\/bioinformatics\/btv692","article-title":"An integrative somatic mutation analysis to identify pathways linked with survival outcomes across 19 cancer types","volume":"32","author":"Park","year":"2016","journal-title":"Bioinformatics"},{"key":"2024011119381898300_ref24","doi-asserted-by":"crossref","first-page":"e1005573","DOI":"10.1371\/journal.pcbi.1005573","article-title":"NetNorM: capturing cancer-relevant information in somatic exome mutation data with gene networks for cancer stratification and prognosis","volume":"13","author":"Le Morvan","year":"2017","journal-title":"PLoS Comput Biol"},{"key":"2024011119381898300_ref25","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1038\/s42256-019-0037-0","article-title":"Clustering single-cell RNA-seq data with a model-based deep learning approach","volume":"1","author":"Tian","year":"2019","journal-title":"Nat Mach Intell"},{"key":"2024011119381898300_ref26","doi-asserted-by":"crossref","first-page":"1196","DOI":"10.1038\/s41592-023-01933-9","article-title":"Significance analysis for clustering with single-cell RNA-sequencing data","volume":"20","author":"Grabski","year":"2023","journal-title":"Nat Methods"},{"key":"2024011119381898300_ref27","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.neunet.2022.08.006","article-title":"Structure enhanced deep clustering network via a weighted neighbourhood auto-encoder","volume":"155","author":"Bai","year":"2022","journal-title":"Neural Netw"},{"key":"2024011119381898300_ref28","doi-asserted-by":"crossref","first-page":"1400","DOI":"10.1145\/3366423.3380214","volume-title":"Proceedings of the Web Conference 2020","author":"Bo","year":"2020"},{"key":"2024011119381898300_ref29","doi-asserted-by":"crossref","first-page":"bbac018","DOI":"10.1093\/bib\/bbac018","article-title":"Deep structural clustering for single-cell RNA-seq data jointly through autoencoder and graph neural network","volume":"23","author":"Gan","year":"2022","journal-title":"Brief Bioinform"},{"key":"2024011119381898300_ref30","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.jmoldx.2014.12.006","article-title":"Memorial Sloan Kettering-integrated mutation profiling of actionable cancer Targets (MSK-IMPACT): a hybridization capture-based next-generation sequencing clinical assay for solid tumor molecular oncology","volume":"17","author":"Cheng","year":"2015","journal-title":"J Mol Diagn"},{"key":"2024011119381898300_ref31","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1186\/s12920-017-0271-4","article-title":"Comprehensive detection of germline variants by MSK-IMPACT, a clinical diagnostic platform for solid tumor molecular oncology and concurrent cancer predisposition testing","volume":"10","author":"Cheng","year":"2017","journal-title":"BMC Med Genomics"},{"key":"2024011119381898300_ref32","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1038\/s43018-021-00172-1","article-title":"Prospective pan-cancer germline testing using MSK-IMPACT informs clinical translation in 751 patients with pediatric solid tumors","volume":"2","author":"Fiala","year":"2021","journal-title":"Nat Cancer"},{"key":"2024011119381898300_ref33","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1016\/j.cell.2022.01.003","article-title":"Genomic characterization of metastatic patterns from prospective clinical sequencing of 25,000 patients","volume":"185","author":"Nguyen","year":"2022","journal-title":"Cell"},{"key":"2024011119381898300_ref34","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/1471-2105-14-7","article-title":"GSVA: gene set variation analysis for microarray and RNA-seq data","volume":"14","author":"H\u00e4nzelmann","year":"2013","journal-title":"BMC Bioinformatics"},{"key":"2024011119381898300_ref35","doi-asserted-by":"crossref","first-page":"D607","DOI":"10.1093\/nar\/gky1131","article-title":"STRING v11: protein\u2013protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets","volume":"47","author":"Szklarczyk","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2024011119381898300_ref36","doi-asserted-by":"crossref","first-page":"949","DOI":"10.1016\/j.ajhg.2008.02.013","article-title":"Walking the interactome for prioritization of candidate disease genes","volume":"82","author":"K\u00f6hler","year":"2008","journal-title":"Am J Hum Genet"},{"key":"2024011119381898300_ref37","doi-asserted-by":"crossref","first-page":"5330","DOI":"10.1038\/s41467-018-07232-8","article-title":"Pan-cancer analysis of transcriptional metabolic dysregulation using the cancer genome atlas","volume":"9","author":"Rosario","year":"2018","journal-title":"Nat Commun"},{"key":"2024011119381898300_ref38","doi-asserted-by":"crossref","first-page":"e47","DOI":"10.1093\/nar\/gkv007","article-title":"Limma powers differential expression analyses for RNA-sequencing and microarray studies","volume":"43","author":"Ritchie","year":"2015","journal-title":"Nucleic Acids Res"},{"key":"2024011119381898300_ref39","first-page":"100141","article-title":"clusterProfiler 4.0: a universal enrichment tool for interpreting omics data","volume":"2","author":"Wu","year":"2021","journal-title":"Innovation (Camb)"},{"key":"2024011119381898300_ref40","doi-asserted-by":"crossref","first-page":"15545","DOI":"10.1073\/pnas.0506580102","article-title":"Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles","volume":"102","author":"Subramanian","year":"2005","journal-title":"Proc Natl Acad Sci U S A"},{"key":"2024011119381898300_ref41","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1016\/j.cels.2015.12.004","article-title":"The molecular signatures database hallmark gene set collection","volume":"1","author":"Liberzon","year":"2015","journal-title":"Cell Syst"},{"key":"2024011119381898300_ref42","doi-asserted-by":"crossref","first-page":"180015","DOI":"10.1038\/sdata.2018.15","article-title":"ImmPort, toward repurposing of open access immunological assay data for translational and clinical research","volume":"5","author":"Bhattacharya","year":"2018","journal-title":"Scientific Data"},{"key":"2024011119381898300_ref43","doi-asserted-by":"crossref","first-page":"4023","DOI":"10.1002\/1878-0261.13313","article-title":"Artificial intelligence-driven consensus gene signatures for improving bladder cancer clinical outcomes identified by multi-center integration analysis","volume":"16","author":"Xu","year":"2022","journal-title":"Mol Oncol"},{"key":"2024011119381898300_ref44","doi-asserted-by":"crossref","first-page":"e80150","DOI":"10.7554\/eLife.80150","article-title":"Comprehensive machine-learning survival framework develops a consensus model in large-scale multicenter cohorts for pancreatic cancer","volume":"11","author":"Wang","year":"2022","journal-title":"Elife"},{"key":"2024011119381898300_ref45","doi-asserted-by":"crossref","first-page":"816","DOI":"10.1038\/s41467-022-28421-6","article-title":"Machine learning-based integration develops an immune-derived lncRNA signature for improving outcomes in colorectal cancer","volume":"13","author":"Liu","year":"2022","journal-title":"Nat Commun"},{"key":"2024011119381898300_ref46","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1007\/s13167-023-00327-3","article-title":"Multi-omics identification of an immunogenic cell death-related signature for clear cell renal cell carcinoma in the context of 3P medicine and based on a 101-combination machine learning computational framework","volume":"14","author":"Liu","year":"2023","journal-title":"EPMA J"},{"key":"2024011119381898300_ref47","doi-asserted-by":"crossref","first-page":"1550","DOI":"10.1038\/s41591-018-0136-1","article-title":"Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response","volume":"24","author":"Jiang","year":"2018","journal-title":"Nat Med"},{"key":"2024011119381898300_ref48","doi-asserted-by":"crossref","first-page":"bbab260","DOI":"10.1093\/bib\/bbab260","article-title":"oncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data","volume":"22","author":"Maeser","year":"2021","journal-title":"Brief Bioinform"},{"key":"2024011119381898300_ref49","doi-asserted-by":"crossref","first-page":"e12539","DOI":"10.1200\/JCO.2022.40.16_suppl.e12539","article-title":"Decreased bile acid metabolism and association with prognosis reflecting microbiome in tumor microenvironment involved in cancer cell proliferation in breast cancer","volume":"40","author":"Wu","year":"2022","journal-title":"J Clin Oncol"},{"key":"2024011119381898300_ref50","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1096\/fba.2022-00017","article-title":"A novel fatty acid metabolism-related gene signature predicts the prognosis, tumor immune properties, and immunotherapy response of colon adenocarcinoma patients","volume":"4","author":"Liu","year":"2022","journal-title":"FASEB BioAdv"},{"key":"2024011119381898300_ref51","doi-asserted-by":"crossref","first-page":"996625","DOI":"10.3389\/fgene.2022.996625","article-title":"Prognostic significance and immune landscape of a fatty acid metabolism-related gene signature in colon adenocarcinoma","volume":"13","author":"Liu","year":"2022","journal-title":"Front Genet"},{"key":"2024011119381898300_ref52","doi-asserted-by":"crossref","first-page":"843515","DOI":"10.3389\/fimmu.2022.843515","article-title":"Prognosis and dissection of immunosuppressive microenvironment in breast cancer based on fatty acid metabolism-related signature","volume":"13","author":"Tang","year":"2022","journal-title":"Front Immunol"},{"key":"2024011119381898300_ref53","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1177\/15353702211049220","article-title":"A metabolism-relevant signature as a predictor for prognosis and therapeutic response in pancreatic cancer","volume":"247","author":"Chen","year":"2022","journal-title":"Exp Biol Med (Maywood)"},{"key":"2024011119381898300_ref54","doi-asserted-by":"crossref","first-page":"24228","DOI":"10.18632\/aging.104134","article-title":"The value of a metabolic reprogramming-related gene signature for pancreatic adenocarcinoma prognosis prediction","volume":"12","author":"Tan","year":"2020","journal-title":"Aging"},{"key":"2024011119381898300_ref55","doi-asserted-by":"crossref","first-page":"577420","DOI":"10.3389\/fonc.2020.577420","article-title":"Alterations of lipid metabolism in cancer: implications in prognosis and treatment","volume":"10","author":"Fern\u00e1ndez","year":"2020","journal-title":"Front Oncol"},{"key":"2024011119381898300_ref56","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1093\/bfgp\/elab040","article-title":"Prognostic and predictive value of a metabolic risk score model in breast cancer: an immunogenomic landscape analysis","volume":"21","author":"Su","year":"2022","journal-title":"Brief Funct Genomics"},{"key":"2024011119381898300_ref57","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1038\/nmeth.3337","article-title":"Robust enumeration of cell subsets from tissue expression profiles","volume":"12","author":"Newman","year":"2015","journal-title":"Nat Methods"},{"key":"2024011119381898300_ref58","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1109\/TNB.2018.2842219","article-title":"XGBFEMF: an XGBoost-based framework for essential protein prediction","volume":"17","author":"Zhong","year":"2018","journal-title":"IEEE Trans Nanobioscience"},{"key":"2024011119381898300_ref59","article-title":"XGBoost model for chronic kidney disease diagnosis","volume":"17","author":"Ogunleye","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2024011119381898300_ref60","first-page":"2825","article-title":"Scikit-learn: machine learning in python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J Mach Learn Res"},{"key":"2024011119381898300_ref61","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1038\/s41568-020-0290-x","article-title":"A compendium of mutational cancer driver genes","volume":"20","author":"Mart\u00ednez-Jim\u00e9nez","year":"2020","journal-title":"Nat Rev Cancer"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/25\/1\/bbad430\/55464714\/bbad430.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/25\/1\/bbad430\/55464714\/bbad430.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,11]],"date-time":"2024-01-11T19:39:10Z","timestamp":1705001950000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbad430\/7457348"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,22]]},"references-count":61,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,11,22]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbad430","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024,1,1]]},"published":{"date-parts":[[2023,11,22]]},"article-number":"bbad430"}}