SFFS-MR: a floating search strategy for GRNs inference

Authors:
Fabrício M. Lopes;David C. Martins, Jr.;Junior Barrera;Roberto M. Cesar, Jr.
Affiliations:
Federal University of Technology-Paraná, Brazil and Institute of Mathematics and Statistics, University of São Paulo, Brazil;Federal University of ABC, Brazil;Faculty of Philosophy, Sciences and Letters of Ribeirão Preto, University of São Paulo, Brazil;Institute of Mathematics and Statistics, University of São Paulo, Brazil and CTBE, Brazil
Venue:
PRIB'10 Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics
Year:
2010

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Abstract

An important problem in the bioinformatics field is the inference of gene regulatory networks (GRN) from temporal expression profiles. In general, the main limitations faced by GRN inference methods is the small number of samples with huge dimensionalities and the noisy nature of the expression measurements. In face of these limitations, alternatives are needed to get better accuracy on the GRNs inference problem. In this context, this work addresses this problemby presenting an alternative feature selection method that applies prior knowledge on its search strategy, called SFFS-MR. The proposed search strategy is based on SFFS algorithm, with the inclusion of multiple roots at the beginning of the search, which are defined by the best and worst single results of the SFS algorithm. In this way, the search space traversed is guided by these roots in order to find the predictor genes for a given target gene, specially to better identify genes presenting intrinsically multivariate prediction, without worsening the asymptotical computational cost of the SFFS. Experimental results show that the SFFS-MR provides a better inference accuracy than SFS and SFFS, maintaining a similar robustness of the SFS and SFFS methods. In addition, the SFFS-MR was able to achieve 60% of accuracy on network recovery after only 20 observations from a state space of size 220, thus presenting very good results.