{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T02:46:06Z","timestamp":1778813166415,"version":"3.51.4"},"reference-count":59,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T00:00:00Z","timestamp":1606780800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T00:00:00Z","timestamp":1606780800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T00:00:00Z","timestamp":1606780800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R21LM012618"],"award-info":[{"award-number":["R21LM012618"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Quant. Biol."],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>With improvements in next\u2010generation DNA sequencing technology, lower cost is needed to collect genetic data. More machine learning techniques can be used to help with cancer analysis and diagnosis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We developed an ensemble machine learning system named performance\u2010weighted\u2010voting model for cancer type classification in 6,249 samples across 14 cancer types. Our ensemble system consists of five weak classifiers (logistic regression, SVM, random forest, XGBoost and neural networks). We first used cross\u2010validation to get the predicted results for the five classifiers. The weights of the five weak classifiers can be obtained based on their predictive performance by solving linear regression functions. The final predicted probability of the performance\u2010weighted\u2010voting model for a cancer type can be determined by the summation of each classifier\u2019s weight multiplied by its predicted probability.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Using the somatic mutation count of each gene as the input feature, the overall accuracy of the performance\u2010weighted\u2010voting model reached 71.46%, which was significantly higher than the five weak classifiers and two other ensemble models: the hard\u2010voting model and the soft\u2010voting model. In addition, by analyzing the predictive pattern of the performance\u2010weighted\u2010voting model, we found that in most cancer types, higher tumor mutational burden can improve overall accuracy.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This study has important clinical significance for identifying the origin of cancer, especially for those where the primary cannot be determined. In addition, our model presents a good strategy for using ensemble systems for cancer type classification.<\/jats:p><\/jats:sec>","DOI":"10.1007\/s40484-020-0226-1","type":"journal-article","created":{"date-parts":[[2020,12,7]],"date-time":"2020-12-07T22:03:22Z","timestamp":1607378602000},"page":"347-358","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Performance\u2010weighted\u2010voting model: An ensemble machine learning method for cancer type classification using whole\u2010exome sequencing mutation"],"prefix":"10.1002","volume":"8","author":[{"given":"Yawei","family":"Li","sequence":"first","affiliation":[{"name":"Department of Preventive Medicine Northwestern University Feinberg School of Medicine Chicago IL 60611 USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Preventive Medicine Northwestern University Feinberg School of Medicine Chicago IL 60611 USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2020,12]]},"reference":[{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1038\/nm1087"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1002\/ajmg.10320"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1519556112"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1093\/molbev\/msy231"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1010978107"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41588\u2010019\u20100423\u2010x"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature09515"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ccell.2017.07.005"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMra1303917"},{"key":"e_1_2_9_11_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature08987"},{"key":"e_1_2_9_12_2","doi-asserted-by":"publisher","DOI":"10.1038\/ng.2764"},{"key":"e_1_2_9_13_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41586\u2010020\u20101969\u20106"},{"key":"e_1_2_9_14_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature12477"},{"key":"e_1_2_9_15_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature12625"},{"key":"e_1_2_9_16_2","doi-asserted-by":"publisher","DOI":"10.1093\/clinchem\/38.1.9"},{"key":"e_1_2_9_17_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1600\u20100749.1997.tb00479.x"},{"key":"e_1_2_9_18_2","first-page":"59","article-title":"Applications of machine learning in cancer prediction and prognosis.","volume":"2","author":"Cruz J. A.","year":"2007","journal-title":"Cancer Inform"},{"key":"e_1_2_9_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.csbj.2014.11.005"},{"key":"e_1_2_9_20_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41576\u2010019\u20100122\u20106"},{"key":"e_1_2_9_21_2","unstructured":"Fakoor R. Ladhak F. Nazi A. Huber M.(2013)Using deep learning to enhance cancer diagnosis and classification. In:2018 IEEE International Conference on System Computation Automation and Networking (icscan). IEEE"},{"key":"e_1_2_9_22_2","doi-asserted-by":"publisher","DOI":"10.1038\/nm0102\u201068"},{"key":"e_1_2_9_23_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.97.1.262"},{"key":"e_1_2_9_24_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.286.5439.531"},{"key":"e_1_2_9_25_2","doi-asserted-by":"publisher","DOI":"10.1089\/106652700750050943"},{"key":"e_1_2_9_26_2","doi-asserted-by":"publisher","DOI":"10.1142\/9789813207813_0022"},{"key":"e_1_2_9_27_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiolchem.2004.11.001"},{"key":"e_1_2_9_28_2","doi-asserted-by":"publisher","DOI":"10.1186\/1471\u20102105\u201014\u2010198"},{"key":"e_1_2_9_29_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2019.103247"},{"key":"e_1_2_9_30_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41591\u2010019\u20100729\u20103"},{"key":"e_1_2_9_31_2","doi-asserted-by":"publisher","DOI":"10.1016\/S1470\u20102045(16)30297\u20102"},{"key":"e_1_2_9_32_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12920\u2010015\u20100130\u20100"},{"key":"e_1_2_9_33_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467\u2010019\u201013825\u20108"},{"key":"e_1_2_9_34_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-9326-7"},{"key":"e_1_2_9_35_2","first-page":"S75","article-title":"Ensemble machine learning on gene expression data for cancer classification.","volume":"2","author":"Tan A. C.","year":"2003","journal-title":"Appl. Bioinformatics"},{"key":"e_1_2_9_36_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13073\u2010017\u20100424\u20102"},{"key":"e_1_2_9_37_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2015.12.028"},{"key":"e_1_2_9_38_2","doi-asserted-by":"publisher","DOI":"10.1038\/nrc2795"},{"key":"e_1_2_9_39_2","doi-asserted-by":"publisher","DOI":"10.11131\/2015\/101182"},{"key":"e_1_2_9_40_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.aag0299"},{"key":"e_1_2_9_41_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1013825816443"},{"key":"e_1_2_9_42_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2018.07.034"},{"key":"e_1_2_9_43_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-53034-3"},{"key":"e_1_2_9_44_2","doi-asserted-by":"publisher","DOI":"10.1093\/annonc\/mdi804"},{"key":"e_1_2_9_45_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10552\u2010014\u20100378\u20102"},{"key":"e_1_2_9_46_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jare.2014.11.007"},{"key":"e_1_2_9_47_2","doi-asserted-by":"publisher","DOI":"10.1002\/ijc.25895"},{"key":"e_1_2_9_48_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature20785"},{"key":"e_1_2_9_49_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2011.11.025"},{"key":"e_1_2_9_50_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ccr.2007.12.003"},{"key":"e_1_2_9_51_2","doi-asserted-by":"publisher","DOI":"10.1155\/2015\/573165"},{"key":"e_1_2_9_52_2","doi-asserted-by":"publisher","DOI":"10.1002\/cyto.a.22993"},{"key":"e_1_2_9_53_2","doi-asserted-by":"publisher","DOI":"10.1158\/1535\u20107163.MCT\u201017\u20100386"},{"key":"e_1_2_9_54_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41588\u2010018\u20100312\u20108"},{"key":"e_1_2_9_55_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cels.2018.03.002"},{"key":"e_1_2_9_56_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF00994018"},{"key":"e_1_2_9_57_2","doi-asserted-by":"publisher","DOI":"10.1186\/1471\u20102105\u201015\u2010311"},{"key":"e_1_2_9_58_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_2_9_59_2","doi-asserted-by":"crossref","unstructured":"Chen T.andGuestrin C.(2016)XGBoost: A scalable tree boosting system. In:Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining pp.785\u2013794","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_2_9_60_2","doi-asserted-by":"crossref","unstructured":"Ting F. F.andSim K. S.(2017)Self\u2010regulated multilayer perceptron neural network for breast cancer classification. In: 2017International Conference on Robotics Automation and Sciences (Icoras)","DOI":"10.1109\/ICORAS.2017.8308074"}],"container-title":["Quantitative Biology"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s40484-020-0226-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s40484-020-0226-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s40484-020-0226-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1007\/s40484-020-0226-1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,3]],"date-time":"2025-01-03T09:54:48Z","timestamp":1735898088000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1007\/s40484-020-0226-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12]]},"references-count":59,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["10.1007\/s40484-020-0226-1"],"URL":"https:\/\/doi.org\/10.1007\/s40484-020-0226-1","archive":["Portico"],"relation":{},"ISSN":["2095-4689","2095-4697"],"issn-type":[{"value":"2095-4689","type":"print"},{"value":"2095-4697","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12]]},"assertion":[{"value":"15 July 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 August 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 September 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 December 2020","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}