Module identification from heterogeneous biological data using multiobjective evolutionary algorithms

Authors:
Michael Calonder;Stefan Bleuler;Eckart Zitzler
Affiliations:
Computer Engineering and Networks Laboratory (TIK), ETH Zurich, Switzerland;Computer Engineering and Networks Laboratory (TIK), ETH Zurich, Switzerland;Computer Engineering and Networks Laboratory (TIK), ETH Zurich, Switzerland
Venue:
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Year:
2006

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Abstract

This paper addresses the problem of identifying gene modules on the basis of different types of biological data such as gene expression and protein-protein interaction data. Given one or several genes of interest, the aim is to find a group of genes—containing the prespecified genes—that are maximally similar with respect to all data types and sets under consideration. While existing studies follow an aggregation approach to tackle the problem of data integration in module identification, we here propose a multiobjective evolutionary method that provides several advantages: (i) no overall similarity measure needs to be defined, (ii) the interactions and conflicts between the data sets can be explored, and (iii) arbitrary data types can be integrated. The usefulness of the presented approach is demonstrated on different biological scenarios, also in comparison to standard clustering.