New probabilistic graphical models for genetic regulatory networks studies.
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.
Department of Biological Sciences, Columbia University, MC 2442, New York, NY 10027, USA. jw2256@columbia.edu
Junbai
J
Wang
Leo Wang-Kit
LW
Cheung
Jan
J
Delabie
D000465:Algorithms; D003196:Computer Graphics; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation; D008957:Models, Genetic; D015233:Models, Statistical; D011336:Probability