General Information
| Description | Knowledge extraction from RNA-seq cancer data through equivalent classification rules |
| Homepage | http://dmb.iasi.cnr.it/camur.php |
Publications
CAMUR: Knowledge extraction from RNA-seq cancer data through equivalent classification rules
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.
Related Tools
cBar
Published August 2010
A computer program to distinguish plasmid-derived from chromosome-derived sequence fragments in metagenomics data
PathVar
Published November 2011
Analysis of gene and protein expression variance in cellular pathways using microarray data
GenoExp
Published January 2015
A web tool for predicting gene expression levels from single nucleotide polymorphisms
CSSSCL
Published October 2015
A python package that uses combined sequence similarity scores for accurate taxonomic classification of long and short s...
Genefu
Published November 2015
An R/Bioconductor package for computation of gene expression-based signatures in breast cancer
FAIR Evaluation
Sorry, you must be signed in to evaluate this object.
