title: Improved bayesian network inference using relaxed gene ordering.
abstract: Bayesian Networks (BNs) have become one of the most powerful means of reconstructing signalling pathways in silico. Excessive computational loads limit the applications of BNs to learn larger sized network structures. Recent bioinformatics research found that signalling pathways are likely hierarchically organised. Genes resident in hierarchical layers constitute biological constraint, which can be readily used by BN structural learning algorithms to substantially reduce the computational load. We propose a constrained BN structural learning algorithm that solves the NP-complete computational problem in a heuristic manner. We demonstrate the utility of our algorithm in constructing two important signalling pathways in S. cerevisiae.
authors.affiliation: Department of Computer Science, University of New Orleans, New Orleans, LA 70148, USA. dzhu@cs.uno.edu
authors.forename: Dongxiao
authors.initials: D
authors.lastname: Zhu
authors.forename: Hua
authors.initials: H
authors.lastname: Li
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D020543:Proteome / Q000378:metabolism; D012441:Saccharomyces cerevisiae / Q000378:metabolism; D015398:Signal Transduction / Q000235:genetics*
chemical_list: D020543:Proteome