CAMUR: Knowledge extraction from RNA-seq cancer data through equivalent classification rules.

ABSTRACT:

Motivation

Nowadays, knowledge extraction methods from Next Generation Sequencing data are highly requested. In this work, we focus on RNA-seq gene expression analysis and specifically on case-control studies with rule-based supervised classification algorithms that build a model able to discriminate cases from controls. State of the art algorithms compute a single classification model that contains few features (genes). On the contrary, our goal is to elicit a higher amount of knowledge by computing many classification models, and therefore to identify most of the genes related to the predicted class.

Results

We propose CAMUR, a new method that extracts multiple and equivalent classification models. CAMUR iteratively computes a rule-based classification model, calculates th

SUBMITTER: Cestarelli V 

PROVIDER: S-EPMC4795614 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature