TY - GEN
T1 - Detecting the presence and absence of causal relationships between expression of yeast genes with very few samples
AU - Kang, Eun Yong
AU - Shpitser, Ilya
AU - Ye, Chun
AU - Eskin, Eleazar
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
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U2 - 10.1007/978-3-642-02008-7_33
DO - 10.1007/978-3-642-02008-7_33
M3 - Conference contribution
AN - SCOPUS:67650293353
SN - 9783642020070
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 466
EP - 481
BT - Research in Computational Molecular Biology - 13th Annual International Conference, RECOMB 2009, Proceedings
T2 - 13th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2009
Y2 - 18 May 2009 through 21 May 2009
ER -