title: CAMUR: Knowledge extraction from RNA-seq cancer data through equivalent classification rules.
abstract: 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.
  
  
  
  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.
  
  
  
  dmb.iasi.cnr.it/camur.php
  
  
  
  emanuel@iasi.cnr.it
  
  
  
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Institute of Systems Analysis and Computer Science - National Research Council, 00185, Rome, Italy.
authors.forename: Valerio
authors.initials: V
authors.lastname: Cestarelli
authors.affiliation: Institute of Systems Analysis and Computer Science - National Research Council, 00185, Rome, Italy, Department of Computer, Control, and Management Engineering - Sapienza University, 00185, Rome, Italy and.
authors.forename: Giulia
authors.initials: G
authors.lastname: Fiscon
authors.affiliation: Institute of Systems Analysis and Computer Science - National Research Council, 00185, Rome, Italy.
authors.forename: Giovanni
authors.initials: G
authors.lastname: Felici
authors.affiliation: Institute of Systems Analysis and Computer Science - National Research Council, 00185, Rome, Italy.
authors.forename: Paola
authors.initials: P
authors.lastname: Bertolazzi
authors.affiliation: Institute of Systems Analysis and Computer Science - National Research Council, 00185, Rome, Italy, Department of Engineering - Uninettuno International University, Corso Vittorio Emanuele II, 39 - 00186 Rome, Italy.
authors.forename: Emanuel
authors.initials: E
authors.lastname: Weitschek
mesh_terms: D000465:Algorithms; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D009369:Neoplasms; D012313:RNA; D017423:Sequence Analysis, RNA
chemical_list: D012313:RNA