Ranking genes based on kernels

Authors:
Nantia Iakovidou;Alexandros Nanopoulos;Yannis Manolopoulos
Affiliations:
Department of Informatics, Aristotle University, Thessaloniki, Greece;Institute of Computer Science, University of Hildesheim, Hildesheim, Germany;Department of Informatics, Aristotle University, Thessaloniki, Greece
Venue:
Intelligent Decision Technologies - Special issue on advances in medical intelligent decision support systems
Year:
2009

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Abstract

Retrieval queries in microarray databases can rank genes according either to their similarity by detecting functionally related genes, or to their importance by detecting genes with significant regulation role. Although both rankings are useful, they can be contradicting. For instance, Similar highly ranked genes may have low importance and vice versa. Thus, we propose a Web-inspired kernel method for fusing the two rankings according to the user needs.