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

ABSTRACT:

Background

With improvements in next-generation 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.

Methods

We developed an ensemble machine learning system named performance-weighted-voting 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-validation 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-weighted

SUBMITTER: Li Y 

PROVIDER: S-EPMC8320732 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature