Mining pathway signatures from microarray data and relevant biological knowledge

  • Authors:

  • Eleftherios Panteris;Stephen Swift;Annette Payne;Xiaohui Liu

  • Affiliations:

  • School of Information Systems, Computing and Mathematics, Brunel University, Uxbridge, Middlesex UB8 3PH, UK;School of Information Systems, Computing and Mathematics, Brunel University, Uxbridge, Middlesex UB8 3PH, UK;School of Information Systems, Computing and Mathematics, Brunel University, Uxbridge, Middlesex UB8 3PH, UK;School of Information Systems, Computing and Mathematics, Brunel University, Uxbridge, Middlesex UB8 3PH, UK

  • Venue:
  • Journal of Biomedical Informatics
  • Year:
  • 2007

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Abstract

High-throughput technologies such as DNA microarray are in the process of revolutionising the way modern biological research is being done. Bioinformatics tools are becoming increasingly important to assist biomedical scientists in their quest in understanding complex biological processes. Gene expression analysis has attracted a large amount of attention over the last few years mostly in the form of algorithms, exploring cluster and regulatory relationships among genes of interest, and programs that try to display the multidimensional microarray data in appropriate formats so that they make biological sense. To reduce the dimensionality of microarray data and make the corresponding analysis more biologically relevant, in this paper we propose a biologically-led approach to biochemical pathway analysis using microarray data and relevant biological knowledge. The method selects a subset of genes for each pathway that describes the behaviour of the pathway at a given experimental condition, and transforms them into pathway signatures. The metabolic pathways of Escherichia coli are used as a case study.