Improvement of Bayesian Network Inference Using a Relaxed Gene Ordering

Dongxiao Zhu, Hua Li

Abstract: Bayesian network structural learning from high throughput data has become a powerful tool in reconstructing signaling pathways. Recent bioinformatics research advocates the notion that signaling networks in the living cell are likely to be hierarchically organized. Genes resident in hierarchical layers constitute biological constraint, which can be readily used by many network structural learning algorithms to reduce the computational complexity. Based on the hierarchical constraint constructed by using breadth-first-search(BFS) on a manually assembled transcriptional regulation network in <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Saccharomyces</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cerevisiae</i> , we propose a new constrained Bayesian network structural learning algorithm that solves the NP-hard computational problem in a heuristic manner. We demonstrate the utility of our algorithm in constructing two important signaling pathways.