New probabilistic graphical models for genetic regulatory networks studies

Authors:
Junbai Wang;Leo Wang-Kit Cheung;Jan Delabie
Affiliations:
Department of Biological Sciences, Columbia University, New York, NY;Epidemiology Section, Cancer Etiology Program, Cancer Research Center of Hawaii, University of Hawaii, Honolulu, HI;Department of Pathology, The Norwegian Radium Hospital, Oslo, Norway
Venue:
Journal of Biomedical Informatics
Year:
2005

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Abstract

This paper introduces two new probabilistic graphical models for reconstruction of genetic regulatory networks using DNA microarray data. One is an independence graph (IG) model with either a forward or a backward search algorithm and the other one is a Gaussian network (GN) model with a novel greedy search method. The performances of both models were evaluated on four MAPK pathways in yeast and three simulated data sets. Generally, an IG model provides a sparse graph but a GN model produces a dense graph where more information about gene-gene interactions may be preserved. The results of our proposed models were compared with several other commonly used models, and our models have shown to give superior performance. Additionally, we found the same common limitations in the prediction of genetic regulatory networks when using only DNA microarray data.