title: Dynamic algorithm for inferring qualitative models of Gene Regulatory Networks.
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 x N x 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.
authors.affiliation: Bioinformatics Research Center, School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798. pg04325488@ntu.edu.sg
authors.forename: Yun
authors.initials: Y
authors.lastname: Zheng
authors.forename: Chee Keong
authors.initials: CK
authors.lastname: Kwoh
mesh_terms: D000465:Algorithms; D053263:Gene Regulatory Networks; D007257:Information Theory; D008957:Models, Genetic; D012441:Saccharomyces cerevisiae / Q000235:genetics