Facing Affymetrix chips analysis through the Data Mining Framework: An application in Parkinson's gene expression experiment

GERMÁN GONZÁLEZ; CELIA LARRAMENDY; OSCAR GERSHANIK; ELMER FERNÁNDEZ

Bernal, Quilmes, Buenos Aires

Congreso; Primer Congreso Argentino de Bioinformática y Biología Computacional; 2010

Asociación Argentina de Bioinformática y Biología Computacional

The Knowledge Discovery in Databases process provides a suitable framework for data analysis in biology. Nowadays, High-throughput genomic technologies, such as microarrays, generate a massive amount of data with complex structure which requires the use of appropriate methods and tools to find relevant biological knowledge from them. Here we show the use of the Unified Analytical Process (UAP) for Data Mining (DM) to analyze a microarray experiment conducted to compare the gene effects of two known Parkinson´s drugs (L-Dopa and Pramipexole (PRA)).Both are widely used Parkinson´s disease (PD) clinics. We find the UAP-DM to be a very useful framework to face microarray experiments, yielding to an ordered workflow of steps that allow a comprehensive analysis of all the aspects related to the microarray experiment, such as design, quality assurance, technical feedback, differential expression analysis and knowledge deployment. In particular it was possible to find relevant and useful gene sets that bring light to now hypothesis in the way those drugs affect Parkinson´s disease.