Detecting the presence and absence of causal relationships between expression of yeast genes with very few samples

Eun Yong Kang, Ilya Shpitser, Chun Ye, Eleazar Eskin

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

Abstract

Inference of biological networks from high-throughput data is a central problem in bioinformatics. Particularly powerful for network reconstruction is data collected by recent studies that contain both genetic variation information and gene expression profiles from genetically distinct strains of an organism. Various statistical approaches have been applied to these data to tease out the underlying biological networks that govern how individual genetic variation mediates gene expression and how genes regulate and interact with each other. Extracting meaningful causal relationships from these networks remains a challenging but important problem. In this paper we use causal inference techniques to infer the presence or absence of causal relationships between yeast gene expressions in the framework of graphical causal models. We evaluate our method using a well studied dataset consisting of both genetic variation information and gene expressions collected over yeast strains. Our predictions of causal regulators are consistent with previously known experimental evidence. In addition, our method can distinguish between direct and indirect effects of variation on a gene expression level.

Original languageEnglish (US)
Title of host publicationResearch in Computational Molecular Biology - 13th Annual International Conference, RECOMB 2009, Proceedings
Pages466-481
Number of pages16
DOIs
StatePublished - 2009
Externally publishedYes
Event13th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2009 - Tucson, AZ, United States
Duration: May 18 2009May 21 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5541 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other13th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2009
Country/TerritoryUnited States
CityTucson, AZ
Period5/18/095/21/09

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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