Ensemble of rankers for efficient gene signature extraction in smoke exposure classification.

ABSTRACT:

Background

System toxicology aims at understanding the mechanisms used by biological systems to respond to toxicants. Such understanding can be leveraged to assess the risk of chemicals, drugs, and consumer products in living organisms. In system toxicology, machine learning techniques and methodologies are applied to develop prediction models for classification of toxicant exposure of biological systems. Gene expression data (RNA/DNA microarray) are often used to develop such prediction models.

Results

The outcome of the present work is an experimental methodology to develop prediction models, based on robust gene signatures, for the classification of cigarette smoke exposure and cessation in humans. It is a result of the participation in the recent sbv IMPROVER SysTox Comp

SUBMITTER: Giordano M 

PROVIDER: S-EPMC5850943 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature