Pipeline design to identify key features and classify the chemotherapy response on lung cancer patients using large-scale genetic data.

ABSTRACT:

Background

During the last decade, the interest to apply machine learning algorithms to genomic data has increased in many bioinformatics applications. Analyzing this type of data entails difficulties for managing high-dimensional data, class imbalance for knowledge extraction, identifying important features and classifying individuals. In this study, we propose a general framework to tackle these challenges with different machine learning algorithms and techniques. We apply the configuration of this framework on lung cancer patients, identifying genetic signatures for classifying response to drug treatment response. We intersect these relevant SNPs with the GWAS Catalog of the National Human Genome Research Institute and explore the Regulomedb, GTEx databases for functional analys

SUBMITTER: Valdes MG 

PROVIDER: S-EPMC6245589 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature