Original Article

Medical & Biological Engineering & Computing

, Volume 45, Issue 12, pp 1175-1185

An effective non-parametric method for globally clustering genes from expression profiles

  • Jingyu HouAffiliated withSchool of Engineering and Information Technology, Deakin University Email author 
  • , Wei ShiAffiliated withThe Walter and Eliza Hall Institute of Medical Research (WEHI)
  • , Gang LiAffiliated withSchool of Engineering and Information Technology, Deakin University
  • , Wanlei ZhouAffiliated withSchool of Engineering and Information Technology, Deakin University

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Abstract

Clustering is widely used in bioinformatics to find gene correlation patterns. Although many algorithms have been proposed, these are usually confronted with difficulties in meeting the requirements of both automation and high quality. In this paper, we propose a novel algorithm for clustering genes from their expression profiles. The unique features of the proposed algorithm are twofold: it takes into consideration global, rather than local, gene correlation information in clustering processes; and it incorporates clustering quality measurement into the clustering processes to implement non-parametric, automatic and global optimal gene clustering. The evaluation on simulated and real gene data sets demonstrates the effectiveness of the algorithm.

Keywords

Bioinformatics Microarray Gene expression Clustering Data mining