Clustering mixed numerical and low quality categorical data: significance metrics on a yeast example
Proceedings of the 2nd international workshop on Information quality in information systems
Formulating and testing hypotheses in functional genomics
Artificial Intelligence in Medicine
Finding microarray genes using GO ontology
Proceedings of the 1st Amrita ACM-W Celebration on Women in Computing in India
Interpreting microarray experiments via co-expressed gene groups analysis (CGGA)
DS'06 Proceedings of the 9th international conference on Discovery Science
Hi-index | 3.84 |
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/ Supplementary information: Supplementary tables as well as files containing the biological data used in this publication can be downloaded from our website http://bioinfo.unice.fr/publications/thea_article/