Module Identification from Heterogeneous Biological Data Using Multiobjective Evolutionary Algorithms

  • Michael Calonder
  • Stefan Bleuler
  • Eckart Zitzler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4193)


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.


Local Search Data Type Multiobjective Optimization Aggregation Function Query Gene 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Michael Calonder
    • 1
  • Stefan Bleuler
    • 1
  • Eckart Zitzler
    • 1
  1. 1.Computer Engineering and Networks Laboratory (TIK)ETH ZurichSwitzerland

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