Biochemical pathway analysis via signature mining

  • Authors:

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

  • Affiliations:

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

  • Venue:
  • CompLife'05 Proceedings of the First international conference on Computational Life Sciences
  • Year:
  • 2005

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Abstract

Biology has been revolutionised by microarrays and bioinformatics is now a powerful tool in the hands of biologists. Gene expression analysis is at the centre of attention over the last few years mostly in the form of algorithms, exploring cluster relationships and dynamic interactions between gene variables, and programs that try to display the multidimensional microarray data in appropriate formats so that they make biological sense. In this paper we propose a simple yet effective approach to biochemical pathway analysis based on biological knowledge. This approach, based on the concept of signature and heuristic search methods such as hill climbing and simulated annealing, is developed to select a subset of genes for each pathway that fully describes the behaviour of the pathway at a given experimental condition in a bid to reduce the dimensionality of microarray data and make the analysis more biologically relevant.