Functional profiling of microarray experiments using text-mining derived bioentities.
The increasing use of microarray technologies brought about a parallel demand in methods for the functional interpretation of the results. Beyond the conventional functional annotations for genes, such as gene ontology, pathways, etc. other sources of information are still to be exploited. Text-mining methods allow extracting informative terms (bioentities) with different functional, chemical, clinical, etc. meanings, that can be associated to genes. We show how to use these associations within an appropriate statistical framework and how to apply them through easy-to-use, web-based environments to the functional interpretation of microarray experiments. Functional enrichment and gene set enrichment tests using bioentities are presented.
Minguez
Pablo
P
Department of Bioinformatics and Functional Genomics Node, (INB), Centro de Investigación Príncipe Felipe (CIPF), Valencia, E46013, Spain.
Al-Shahrour
Fátima
F
Montaner
David
D
Dopazo
Joaquín
J
D001185:Artificial Intelligence; D030562:Databases, Protein; D020869:Gene Expression Profiling / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D009323:Natural Language Processing; D011506:Proteins / Q000145:classification* / Q000378:metabolism*; D012107:Research Design; D018511:Systems Integration
D011506:Proteins