Cancer classification and pathway discovery using non-negative matrix factorization.

ABSTRACT: OBJECTIVES:Extracting genetic information from a full range of sequencing data is important for understanding disease. We propose a novel method to effectively explore the landscape of genetic mutations and aggregate them to predict cancer type. DESIGN:We applied non-smooth non-negative matrix factorization (nsNMF) and support vector machine (SVM) to utilize the full range of sequencing data, aiming to better aggregate genetic mutations and improve their power to predict disease type. More specifically, we introduce a novel classifier to distinguish cancer types using somatic mutations obtained from whole-exome sequencing data. Mutations were identified from multiple cancers and scored using SIFT, PP2, and CADD, and collapsed at the individual gene level. nsNMF was then applied to reduce d

SUBMITTER: Zeng Z 

PROVIDER: S-EPMC6697569 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature