Labeling negative examples in supervised learning of new gene regulatory connections

Luigi Cerulo, Vincenzo Paduano, Pietro Zoppoli, Michele Ceccarelli

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Supervised learning methods have been recently exploited to learn gene regulatory networks from gene expression data. The basic approach consists into building a binary classifier from feature vectors composed by expression levels of a set of known regulatory connections, available in public databases or known in literature. Such a classifier is then used to predict new unknown connections. The quality of the training set plays a crucial role in such an inference scheme. In binary classification the training set should be composed of positive and negative examples, but in Biology literature the only collected information is whether two genes interact. Instead, the counterpart information is usually not reported, as Biologists are not aware to state whether two genes are not interacting. The over presence of topology motifs in currently known gene regulatory networks, such as, feed-forward loops, bi-fan clusters, and single input modules, could drive the selection of reliable negative examples. We introduce, discuss, and evaluate a number of negative selection heuristics that exploits the known gene network topology of Escherichia coli and Saccharomyces cerevisiae.

Original languageEnglish
Title of host publicationComputational Intelligence Methods for Bioinformatics and Biostatistics - 7th International Meeting, CIBB 2010, Revised Selected Papers
Pages159-173
Number of pages15
DOIs
Publication statusPublished - 19 Aug 2011
Event7th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2010 - Palermo, Italy
Duration: 16 Sep 201018 Sep 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6685 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other7th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2010
CountryItaly
CityPalermo
Period16/9/1018/9/10

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Keywords

  • positive only
  • reverse engineering gene regulatory networks
  • supervised learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Cerulo, L., Paduano, V., Zoppoli, P., & Ceccarelli, M. (2011). Labeling negative examples in supervised learning of new gene regulatory connections. In Computational Intelligence Methods for Bioinformatics and Biostatistics - 7th International Meeting, CIBB 2010, Revised Selected Papers (pp. 159-173). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6685 LNBI). https://doi.org/10.1007/978-3-642-21946-7_13