Claude Pasquier

GenMiner: mining non-redundant association rules from integrated gene expression data and annotations

UNLABELLED GenMiner is an implementation of association rule discovery dedicated to the analysis of genomic data. It allows the analysis of datasets integrating multiple sources of biological data represented as both discrete values, such as gene annotations, and continuous values, such as gene expression measures. GenMiner implements the new NorDi (normal discretization) algorithm for normalizing and discretizing continuous values and takes advantage of the Close algorithm to efficiently generate minimal non-redundant association rules. Experiments show that execution time and memory usage of GenMiner are significantly smaller than those of the standard Apriori-based approach, as well as the number of extracted association rules. AVAILABILITY The GenMiner software and supplementary materials are available at http://bioinfo.unice.fr/publications/genminer_article/ and http://keia.i3s.unice.fr/?Implementations:GenMiner SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

THEA: ontology-driven analysis of microarray data

MOTIVATION Microarray technology makes it possible to measure thousands of variables and to compare their values under hundreds of conditions. Once microarray data are quantified, normalized and classified, the analysis phase is essentially a manual and subjective task based on visual inspection of classes in the light of the vast amount of information available. Currently, data interpretation clearly constitutes the bottleneck of such analyses and there is an obvious need for tools able to fill the gap between data processed with mathematical methods and existing biological knowledge. RESULTS THEA (Tools for High-throughput Experiments Analysis) is an integrated information processing system allowing convenient handling of data. It allows to automatically annotate data issued from classification systems with selected biological information coming from a knowledge base and to either manually search and browse through these annotations or automatically generate meaningful generalizations according to statistical criteria (data mining). AVAILABILITY The software is available on the website http://thea.unice.fr/

GenMiner: Mining Informative Association Rules from Genomic Data

GENMINER is a smart adaptation of closed itemsets based association rules extraction to genomic data. It takes advantage of the novel NORDI discretization method and of the CLOSE [27] algorithm to efficiently generate min- imal non-redundant association rules. GENMINER facili- tates the integration of numerous sources of biological in- formation such as gene expressions and annotations, and can tacitly integrate qualitative information on biological conditions (age, sex, etc.). We validated this approach ana- lyzing the microarray datasets used by Eisen et al. [10] with several sources of biological annotations. Extracted asso- ciations revealed significant co-annotated and co-expressed gene patterns, showing important biological relationships between genes and their features. Several of these relation- ships are supported by recent biological literature.

Co-expressed gene groups analysis (CGGA): An automatic tool for the interpretation of microarray experiments

Summary Microarray technology produces vast amounts of data by measuring simultaneously the expression levels of thousands of genes under hundreds of biological conditions. Nowadays, one of the principal challenges in bioinformatics is the interpretation of this large amount of data using different sources of information. We have developed a novel data analysis method named CGGA (Co-expressed Gene Groups Analysis) that automatically finds groups of genes that are functionally enriched, i.e. have the same functional annotations, and are co-expressed. CGGA automatically integrates the information of microarrays, i.e. gene expression profiles, with the functional annotations of the genes obtained by the genome-wide information sources such as Gene Ontology. By applying CGGA to wellknown microarray experiments, we have identified the principal functionally enriched and co-expressed gene groups, and we have shown that this approach enhances and accelerates the interpretation of DNA microarray experiments.

Mining Association Rule Bases from Integrated Genomic Data and Annotations

During the last decade, several clustering and association rule mining techniques have been applied to highlight groups of co-regulated genes in gene expression data. Nowadays, integrating these data and biological knowledge into a single framework has become a major challenge to improve the relevance of mined patterns and simplify their interpretation by biologists. GenMiner was developed for mining association rules from such integrated datasets. It combines a new nomalized discretization method, called NorDi, and the JClose algorithm to extract condensed representations for association rules. Experimental results show that GenMiner requires less memory than Apriori based approaches and that it improves the relevance of extracted rules. Moreover, association rules obtained revealed significant co-annotated and co-expressed gene patterns showing important biological relationships supported by recent biological literature.

Interpreting microarray experiments via co-expressed gene groups analysis (CGGA)

Microarray technology produces vast amounts of data by measuring simultaneously the expression levels of thousands of genes under hundreds of biological conditions. Nowadays, one of the principal challenges in bioinformatics is the interpretation of huge data using different sources of information. We propose a novel data analysis method named CGGA (Co-expressed Gene Groups Analysis) that automatically finds groups of genes that are functionally enriched, i.e. have the same functional annotations, and are co-expressed. CGGA automatically integrates the information of microarrays, i.e. gene expression profiles, with the functional annotations of the genes obtained by the genome-wide information sources such as Gene Ontology (GO). By applying CGGA to well-known microarray experiments, we have identified the principal functionally enriched and co-expressed gene groups, and we have shown that this approach enhances and accelerates the interpretation of DNA microarray experiments.