Formulating and testing hypotheses in functional genomics

Authors:
Joaquin Dopazo
Affiliations:
Department of Bioinformatics, and Functional Genomics Node (INB), Valencia E-46013, Spain and Centro de Investigación Príncipe Felipe, Valencia E-46013, Spain and CIBER de Enfermedades R ...
Venue:
Artificial Intelligence in Medicine
Year:
2009

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Guest editorial: Computational intelligence and machine learning in bioinformatics

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Abstract

Objective: The ultimate goal of any genome-scale experiment is to provide a functional interpretation of the results, relating the available genomic information to the hypotheses that originated the experiment. Methods and results: Initially, this interpretation has been made on a pre-selection of relevant genes, based on the experimental values, followed by the study of the enrichment in some functional properties. Nevertheless, functional enrichment methods, demonstrated to have a flaw: the first step of gene selection was too stringent given that the cooperation among genes was ignored. The assumption that modules of genes related by relevant biological properties (functionality, co-regulation, chromosomal location, etc.) are the real actors of the cell biology lead to the development of new procedures, inspired in systems biology criteria, generically known as gene-set methods. These methods have been successfully used to analyze transcriptomic and large-scale genotyping experiments as well as to test other different genome-scale hypothesis in other fields such as phylogenomics.