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Performance-weighted-voting model:An ensemble machine learning method for cancer type classification using whole-exome sequencing mutation

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Quantitative Biology 2020年第4期8卷 347-358页

作者： Yawei Li Yuan Luo Department of Preventive Medicine Northwestern UniversityFeinberg School of MedicineChicagoIL 60611USA

Background:With improvements in next-generation DNA sequencing technology,lower cost is needed to collect genetic *** machine learning techniques can be used to help with cancer analysis and ***:We developed an ensemble machine learning system named performance-weighted-voting model for cancer type classification in 6,249 samples across 14 cancer *** ensemble system consists of five weak classifiers(logistic regression,SVM,random forest,XGBoost and neural networks).We first used cross-validation to get the predicted results for the five *** weights of the five weak classifiers can be obtained based on their predictive performance by solving linear regression *** final predicted probability of the performance-weighted-voting model for a cancer type can be determined by the summation of each classifier's weight multiplied by its predicted ***:Using the somatic mutation count of each gene as the input feature,the overall accuracy of the performance-weighted-voting model reached 71.46%,which was significantly higher than the five weak classifiers and two other ensemble models:the hard-voting model and the soft-voting *** addition,by analyzing the predictive pattern of the performance-weighted-voting model,we found that in most cancer types,higher tumor mutational burden can improve overall ***:This study has important clinical significance for identifying the origin of cancer,especially for those where the primary cannot be *** addition,our model presents a good strategy for using ensemble systems for cancer type classification.

关键词： cancer type classification ensemble method performance-weighted-voting model linear regression single-nucleotide polymorphism

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