Title: A supervised learning approach to the ensemble clustering of genes

Authors: Andrew K. Rider; Geoffrey Siwo; Scott J. Emrich; Michael T. Ferdig; Nitesh V. Chawla

Addresses: Department of Computer Science and Engineering, Eck Institute for Global Health, Interdisciplinary Center for Network Science and Applications, Notre Dame IN 46556, USA ' Department of Biological Sciences, Eck Institute for Global Health, Notre Dame IN 46556, USA ' Department of Computer Science and Engineering, Eck Institute for Global Health, Notre Dame IN 46556, USA ' Department of Biological Sciences, Eck Institute for Global Health, Interdisciplinary Center for Network Science and Applications, Notre Dame IN 46556, USA ' Department of Computer Science and Engineering, Eck Institute for Global Health, Interdisciplinary Center for Network Science and Applications, Notre Dame IN 46556, USA

Abstract: High-throughput techniques have become a primary approach to gathering biological data. These data can be used to explore relationships between genes and guide development of drugs and other research. However, the deluge of data contains an overwhelming amount of unknown information about the organism under study. Therefore, clustering is a common first step in the exploratory analysis of high-throughput biological data. We present a supervised learning approach to clustering that utilises known gene-gene interaction data to improve results for already commonly used clustering techniques. The approach creates an ensemble similarity measure that can be used as input to any clustering technique and provides results with increased biological significance while not altering the clustering method.

Keywords: ensemble clustering; gene clustering; supervised learning; random subspaces; microarrays; high-throughput biological data; gene-gene interactions; bioinformatics.

DOI: 10.1504/IJDMB.2014.059062

International Journal of Data Mining and Bioinformatics, 2014 Vol.9 No.2, pp.199 - 219

Received: 31 May 2011
Accepted: 16 Jun 2011

Published online: 02 Oct 2013 *

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