Identification of the Compound Subjective Rule Interestingness Measure for Rule-Based Functional Description of Genes

  • Aleksandra Gruca
  • Marek Sikora
Conference paper

DOI: 10.1007/978-3-642-33185-5_14

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7557)
Cite this paper as:
Gruca A., Sikora M. (2012) Identification of the Compound Subjective Rule Interestingness Measure for Rule-Based Functional Description of Genes. In: Ramsay A., Agre G. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2012. Lecture Notes in Computer Science, vol 7557. Springer, Berlin, Heidelberg

Abstract

Methods for automatic functional description of gene groups are useful tools supporting the interpretation of biological experiments. The RuleGO algorithm provides functional interpretation of gene groups in a form of logical rules including combinations of Gene Ontology terms in their premises. The number of rules generated by the algorithm is usually huge and additional methods of rule quality evaluation and filtration are required in order to select the most interesting ones. In the paper, we apply the multicriteria decision making UTA method to obtain a ranking of rules based on subjective expert opinion which is provided in a form of an ordered list of several rules. The presented approach is applied to the well known data set from microarray experiment and the results are compared with the standard RuleGO compound rule quality measure.

Keywords

rule quality rule interestingness multicriteria decision making functional annotations Gene Ontology bioinformatics 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Aleksandra Gruca
    • 1
  • Marek Sikora
    • 1
    • 2
  1. 1.Silesian University of TechnologyGliwicePoland
  2. 2.Institute of Innovative Technologies EMAGKatowicePoland

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