Authors: Yun Zheng, Chee Keong Kwoh
Addresses: 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
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.
International Journal of Data Mining and Bioinformatics, 2006 Vol.1 No.2, pp.111 - 137
Published online: 07 Sep 2006 *Full-text access for editors Access for subscribers Purchase this article Comment on this article