Optimal Use of Biological Expert Knowledge from Literature Mining in Ant Colony Optimization for Analysis of Epistasis in Human Disease

  • Arvis Sulovari
  • Jeff Kiralis
  • Jason H. Moore
Conference paper

DOI: 10.1007/978-3-642-37189-9_12

Volume 7833 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Sulovari A., Kiralis J., Moore J.H. (2013) Optimal Use of Biological Expert Knowledge from Literature Mining in Ant Colony Optimization for Analysis of Epistasis in Human Disease. In: Vanneschi L., Bush W.S., Giacobini M. (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2013. Lecture Notes in Computer Science, vol 7833. Springer, Berlin, Heidelberg

Abstract

The fast measurement of millions of sequence variations across the genome is possible with the current technology. As a result, a difficult challenge arise in bioinformatics: the identification of combinations of interacting DNA sequence variations predictive of common disease [1]. The Multifactor Dimensionality Reduction (MDR) method is capable of analysing such interactions but an exhaustive MDR search would require exponential time. Thus, we use the Ant Colony Optimization (ACO) as a stochastic wrapper. It has been shown by Greene et al. that this approach, if expert knowledge is incorporated, is effective for analysing large amounts of genetic variation[2]. In the ACO method integrated in the MDR package, a linear and an exponential probability distribution function can be used to weigh the expert knowledge. We generate our biological expert knowledge from a network of gene-gene interactions produced by a literature mining platform, Pathway Studio. We show that the linear distribution function of expert knowledge is the most appropriate to weigh our scores when expert knowledge from literature mining is used. We find that ACO parameters significantly affect the power of the method and we suggest values for these parameters that can be used to optimize MDR in Genome Wide Association Studies that use biological expert knowledge.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Arvis Sulovari
    • 1
  • Jeff Kiralis
    • 1
  • Jason H. Moore
    • 1
  1. 1.Dartmouth-Hitchcock Medical CenterLebanonUnited States