Learning genetic regulatory network connectivity from time series data

Nathan Barker*, Chris Myere, Hiroyuki Kuwahara

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Recent experimental advances facilitate the collection of time series data that indicate which genes in a cell are expressed. This paper proposes an efficient method to generate the genetic regulatory network inferred from time series data. Our method first encodes the data into levels. Next, it determines the set of potential parents for each gene based upon the probability of the gene's expression increasing. After a subset of potential parents are selected, it determines if any genes in this set may have a combined effect. Finally, the potential sets of parents are competed against each other to determine the final set of parents. The result is a directed graph representation of the genetic network's repression and activation connections. Our results on synthetic data generated from models for several genetic networks with tight feedback are promising.

Original languageEnglish (US)
Title of host publicationAdvances in Applied Artificial Intelligence - 19th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2006, Proceedings
PublisherSpringer Verlag
Pages962-971
Number of pages10
ISBN (Print)3540354530, 9783540354536
DOIs
StatePublished - Jan 1 2006
Event19th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2006 - Annecy, France
Duration: Jun 27 2006Jun 30 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4031 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other19th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2006
CountryFrance
CityAnnecy
Period06/27/0606/30/06

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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