geneCBR Gene clustering made easy.
Download

geneCBR Ranking & Summary

geneCBR Tags

extract knowledge extraction Cluster selection gene expression gene clustering functionality clustering sort gene analyze gene set find gene set extract gene subset gene subset searcher grab gene subset view gene data display gene data gene correlation correlate gene analyze gene gene organization gene visualization compare gene gene comparer gene pattern candidate gene image clustering simulate gene mutation gene adjacency gene annotation gene-reaction-protein network Gene Ontology database Gene Ontology gene network generator gene information management gene information gene product association gene identifiers translator gene function prediction gene identification gene match gene mapper compare gene order gene content derived analyze protein-coding gene gene selection gene clustering analyze 16S rRNA gene sequence ancestral gene cluster gene cluster calculator ancestral gene gene cluster analyze gene markers gene transfer simulate gene network growing gene duplication estimate gene diversities test gene diversities Gene Diversity Gene Sequence gene-gene interaction Gene Sequence Name gene clusters reliability analyze modifier gene map disease gene disease gene mapper visualize gene explore gene view gene

geneCBR Description

geneCBR is an easy to use, Java based bioinformatics application designed to enable the use of combined techniques that can be applied to gene selection, clustering, knowledge extraction and prediction. In diagnostic mode, geneCBR employs a case-based reasoning model that incorporates a set of fuzzy prototypes for the retrieval of relevant genes, a growing cell structure network for the clustering of similar patients and a proportional weighted voting algorithm to provide an accurate diagnosis. Specifically geneCBR is a model that can perform cancer classification based on microarray data. In order to store the information belonging to each sample, the system uses a fuzzy codification to represent the gene expression levels of each sample. This operation permits the generalization over the whole case base in order to tackle intra-experimental and inter-experimental variations in the data. Based on the fuzzy discretization of real gene expression data into a small number of fuzzy membership functions, the system is capable of constructing a set of prototypes that are able to represent the main characteristics of previously ascertained classes.

geneCBR Related Software