Constraint programming in community-based gene regulatory network inference

Ferdinando Fioretto, Enrico Pontelli

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

2 Scopus citations

Abstract

Gene Regulatory Network (GRN) inference is a major objective of Systems Biology. The complexity of biological systems and the lack of adequate data have posed many challenges to the inference problem. Community networks integrate predictions from individual methods in a "meta predictor", in order to compose the advantages of different methods and soften individual limitations. This paper proposes a novel methodology to integrate prediction ensembles using Constraint Programming, a declarative modeling paradigm, which allows the formulation of dependencies among components of the problem, enabling the integration of diverse forms of knowledge. The paper experimentally shows the potential of this method: the addition of biological constraints can offer improvements in the prediction accuracy, and the method shows promising results in assessing biological hypothesis using constraints.

Original languageEnglish (US)
Title of host publicationComputational Methods in Systems Biology - 11th International Conference, CMSB 2013, Proceedings
Pages135-149
Number of pages15
DOIs
StatePublished - 2013
Event11th International Conference on Computational Methods in Systems Biology, CMSB 2013 - Klosterneuburg, Austria
Duration: Sep 22 2013Sep 24 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8130 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th International Conference on Computational Methods in Systems Biology, CMSB 2013
CountryAustria
CityKlosterneuburg
Period9/22/139/24/13

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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