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 the power set of the genes present in the rules, iteratively 
eliminates those combinations from the data set, and performs again the 
classification procedure until a stopping criterion is verified. CAMUR includes 
an ad-hoc knowledge repository (database) and a querying tool.We analyze three 
different types of RNA-seq data sets (Breast, Head and Neck, and Stomach Cancer) 
from The Cancer Genome Atlas (TCGA) and we validate CAMUR and its models also on 
non-TCGA data. Our experimental results show the efficacy of CAMUR: we obtain 
several reliable equivalent classification models, from which the most frequent 
genes, their relationships, and the relation with a particular cancer are 
deduced.
AVAILABILITY AND IMPLEMENTATION: dmb.iasi.cnr.it/camur.php
CONTACT: emanuel@iasi.cnr.it
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics 
online.