title: A sub-space greedy search method for efficient Bayesian Network inference.
abstract: Bayesian network (BN) has been successfully used to infer the regulatory relationships of genes from microarray dataset. However, one major limitation of BN approach is the computational cost because the calculation time grows more than exponentially with the dimension of the dataset. In this paper, we propose a sub-space greedy search method for efficient Bayesian Network inference. Particularly, this method limits the greedy search space by only selecting gene pairs with higher partial correlation coefficients. Using both synthetic and real data, we demonstrate that the proposed method achieved comparable results with standard greedy search method yet saved ∼50% of the computational time. We believe that sub-space search method can be widely used for efficient BN inference in systems biology.
authors: School of Life Sciences and the State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, N. T., Hong Kong SAR, China.
authors: Qing
authors: Q
authors: Zhang
authors: Yong
authors: Y
authors: Cao
authors: Yong
authors: Y
authors: Li
authors: Yanming
authors: Y
authors: Zhu
authors: Samuel S M
authors: SS
authors: Sun
authors: Dianjing
authors: D
authors: Guo
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D053263:Gene Regulatory Networks; D006801:Humans; D015470:Leukemia, Myeloid, Acute / Q000235:genetics / Q000378:metabolism; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D054198:Precursor Cell Lymphoblastic Leukemia-Lymphoma / Q000235:genetics / Q000378:metabolism