id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_pb27dlju2vfnzoojuflkutsnxa	T LIU	Model gene network by semi-fixed Bayesian network	2006	8	.pdf	application/pdf	6800	695	67	Gene networks describe functional pathways in a given cell or tissue, representing processes such as metabolism, gene expression regulation, Thus, learning gene network is a crucial problem in the post genome era. Most existing works learn gene networks We propose a semi-fixed model to represent the gene network as a Bayesian network results and comparison with the-state-of-the-art learning algorithms on artificial and real-life datasets confirm the effectiveness of our approach. Keywords: Gene network; Bayesian networks; Hidden variable; Semi-fixed network; Semi-fixed structure EM learning algorithm over other methods in learning gene network (Friedman et al., propose a hidden variable hj, which represents the combination of a set of regulatory proteins expressed from Pa of the artificial network, which contains 4 genes and 4 regulatory edges. in learning Bayesian network with hidden variables (Friedman, variables to model the important components of gene network, Model gene network as a semi-fixed networkwith hidden variables	./cache/work_pb27dlju2vfnzoojuflkutsnxa.pdf	./txt/work_pb27dlju2vfnzoojuflkutsnxa.txt