Article Abstract

	Title:	Dynamic algorithm for inferring qualitative models of Gene Regulatory Networks
	Author:	Yun Zheng, Chee Keong Kwoh
	Address:	Bioinformatics Research Center, School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, 639798, Singapore. ' Bioinformatics Research Center, School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, 639798, Singapore
	Journal:	International Journal of Data Mining and Bioinformatics 2006 - Vol. 1, No.2  pp. 111 - 137
	Abstract:	We introduce a novel algorithm, DFL (Discrete Function Learning), for reconstructing qualitative models of Gene Regulatory Networks (GRNs) from gene expression data in this paper. We analyse its complexity of O(k · N · n²) on the average and its data requirements. The experiments of synthetic Boolean networks show that the DFL algorithm is more efficient than current algorithms without loss of prediction performances. The results of yeast cell cycle gene expression data show that the DFL algorithm can identify biologically significant models with reasonable accuracy, sensitivity and high precision with respect to the literature evidences.
	Keywords:	algorithms; gene regulatory networks; GRNs; qualitative models; inference; mutual information; entropy; bioinformatics; gene expression data; yeast cell cycle.
	DOI:	10.1504/IJDMB.2006.010851
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