SFFS-SW: A Feature Selection Algorithm Exploring the Small-World Properties of GNs

  • Fábio Fernandes da Rocha Vicente
  • Fabrício Martins Lopes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8626)


In recent years, several methods for gene networks (GNs) inference from expression data have been developed. Also, models of data integration (as protein-protein and protein-DNA) are nowadays broadly used to face the problem of few amount of expression data. Moreover, it is well known that biological networks conserve some topological properties. The small-world topology is a common arrangement in nature found both in biological and non-biological phenomena. However, in general this information is not used by GNs inference methods. In this work we proposed a new GNs inference algorithm that combines topological features and expression data. The algorithm outperforms the approach that uses only expression data both in accuracy and measures of recovered network.


small-world gene networks feature selection graph theory pattern recognition bioinformatics 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Fábio Fernandes da Rocha Vicente
    • 1
    • 2
  • Fabrício Martins Lopes
    • 1
  1. 1.Federal University of TechnologyParanáBrazil
  2. 2.Institute of Mathematics and StatisticsUniversity of São PauloSão PauloBrazil

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