Recurrent neuro-fuzzy network models for reverse engineering gene regulatory interactions

Ioannis Maraziotis, Andrei Dragomir, Anastasios Bezerianos

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

Abstract

Understanding the way gene regulatory networks (complex systems of genes, proteins and other molecules) function and interact to carry out specific cell functions is currently one of the central goals in computational molecular biology. We propose an approach for inferring the complex causal relationships among genes from microarray experimental data based on a recurrent neuro-fuzzy method. The method derives information on the gene interactions in a highly interpretable form (fuzzy rules) and takes into account dynamical aspects of genes regulation through its recurrent structure. The gene interactions retrieved from a set of genes known to be highly regulated during the yeast cell-cycle are validated by biological studies, while our method surpasses previous computational techniques that attempted gene networks reconstruction, being able to retrieve significantly more biologically valid relationships among genes.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages24-34
Number of pages11
DOIs
StatePublished - Dec 1 2005
Event1st International Symposium on Computational Life Sciences, CompLife 2005 - Konstanz, Germany
Duration: Sep 25 2005Sep 27 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3695 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other1st International Symposium on Computational Life Sciences, CompLife 2005
CountryGermany
CityKonstanz
Period9/25/059/27/05

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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