Reverse engineering of regulatory relations in gene networks by a probabilistic approach

Michele Ceccarelli, Sandro Morganella, Pietro Zoppoli

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

Abstract

In the last years microarray technology has revolutionised the fields of genetics, biotechnology and drug discovery. Due to its high parallelity, different analyses can be accomplished in one single experiment to generate vast amounts of data. In this paper we propose a new approach to solve the reverse engineering of regulatory relations task into gene networks from high-throughput data. We develop an Inference of Regulatory Interaction Schema (IRIS) algorithm that uses an iterative method to map gene expression profile values (steady-state and time-course) into discrete states, so that, a probabilistic approach can be used to infer gene interaction rules. IRIS provides two different descriptions of each regulatory relation: the description in which interactions are described as conditional probability tables (CPT-like) and descriptions in which regulations are truth tables (TT-like). We test IRIS on two synthetic networks and on real biological data showing its accuracy and efficiency.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages360-367
Number of pages8
Volume5571 LNAI
DOIs
Publication statusPublished - 27 Aug 2009
Externally publishedYes
Event8th International Workshop on Fuzzy Logic and Applications, WILF 2009 - Palermo, Italy
Duration: 9 Jun 200912 Jun 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5571 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other8th International Workshop on Fuzzy Logic and Applications, WILF 2009
CountryItaly
CityPalermo
Period9/6/0912/6/09

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ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Ceccarelli, M., Morganella, S., & Zoppoli, P. (2009). Reverse engineering of regulatory relations in gene networks by a probabilistic approach. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5571 LNAI, pp. 360-367). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5571 LNAI). https://doi.org/10.1007/978-3-642-02282-1_45