Feature Selection for Breast Cancer Classification by Integrating Somatic Mutation and Gene Expression.

ABSTRACT: Exploring the molecular mechanisms of breast cancer is essential for the early prediction, diagnosis, and treatment of cancer patients. The large scale of data obtained from the high-throughput sequencing technology makes it difficult to identify the driver mutations and a minimal optimal set of genes that are critical to the classification of cancer. In this study, we propose a novel method without any prior information to identify mutated genes associated with breast cancer. For the somatic mutation data, it is processed to a mutated matrix, from which the mutation frequency of each gene can be obtained. By setting a reasonable threshold for the mutation frequency, a mutated gene set is filtered from the mutated matrix. For the gene expression data, it is used to generate the gene expres

SUBMITTER: Jiang Q 

PROVIDER: S-EPMC7952975 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature