We propose a statistical method for estimating a gene network based on Bayesian networks from microarray gene expression data together with biological knowledge including protein-protein interactions, protein-DNA interactions, binding site information, existing literature and so on. Unfortunately, microarray data do not contain enough information for constructing gene networks accurately in many cases. Our method adds biological knowledge to the estimation method of gene networks under a Bayesian statistical framework, and also controls the trade-off between microarray information and biological knowledge automatically. We conduct Monte Carlo simulations to show the effectiveness of the proposed method. We analyze Saccharomyces cerevisiae gene expression data as an application. 1.

Introduction Inference of gene networks from gene expression measurements is a major challenge in Systems Biology. If gene networks can be infered correctly, it can lead to a better understanding of cellular processes, and, therefore, have applications to drug discovery, disease studies, and other areas. Bayesian networks are a widely used approach to model gene networks 3,4,7,9,11,13 ,14,17 . In Bayesian networks, the behaviour of the gene network is modeled as a joint probability distribution for all genes. This allows a very general modeling of gene interactions. The joint probability distribution can be decomposed as a product of conditional probabilities P (X g |X 1 ,...,X n ), representing the regulation of a gene g by some genes g 1 ,...,g n . This decomposition can be represented as a directed acyclic graph. The Bayesian network model has been shown to allow finding biologically plausible gene networks 4,9 . However, the di#culty of learning Bayesian networks lies in

The accurate estimation of gene networks from gene expression measurements is a major challenge in the field of Bioinformatics. Since the problem of estimating gene networks is NP-hard and exhibits a search space of super-exponential size, researchers are using heuristic algorithms for this task. However, little can be said about the accuracy of heuristic estimations. In order to overcome this problem, we present a general approach to reduce the search space to a biologically meaningful subspace and to find optimal solutions within the subspace in linear time. We show the e#ectiveness of this approach in application to yeast and Bacillus subtilis data.

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Disentangling networks of regulation of gene expression is a major challenge in the field of computational biology. Harvesting the information contained in microarray data sets is a promising approach towards this challenge. We propose an algorithm for the optimal estimation of Bayesian networks from microarray data, which reduces the CPU time and memory consumption of previous algorithms. We prove that the space complexity can be reduced from O(n )toO(2 ), and that the expected calculation time can be reduced from O(n )toO(n ), where n is the number of genes. We make intrinsic use of a limitation of the maximal number of regulators of each gene, which has biological as well as statistical justifications. The improvements are significant for some applications in research.

Abstract. Inferring the metabolic pathways that control the cell cycles is a challenging and difficult task. However, its importance in the process of understanding living organisms has been leading to the development of several models to infer gene regulatory networks from DNA microarray data. In the last years, many works have been adding biological information to those models to improve the obtained results. In this work, we add prior biological knowledge into a Bayesian Network model with non parametric regression and analyze the effects caused by such information in the results. 1

We propose a novel statistical method for estimating gene networks based on microarray gene expression data together with information from biological knowledge databases. Although a large amount of gene regulation information has already been stored in some biological databases, there are still errors and missing facts due to experimental problems and human errors. Therefore, we cannot blindly use them for understanding gene regulation and a robust procedure with a statistical model for using such database information is required. By using gene expression data, we provide a probabilistic framework of a joint learning model for repairing database information and for estimating a gene network based on dynamic Bayesian networks, simultaneously. To show the effectiveness of the proposed method, we analyze Saccharomyces cerevisiae cell-cycle gene expression data together with KEGG information.

doi:10.1093/nar/gkm292 BioBayesNet: a web server for feature extraction and Bayesian network modeling of biological sequence data

I would like to express my sincere gratitude to my supervisors, Dr. Sung Wing-Kin, Dr. Mao Pei-Lin and Dr. Liu Bing, for providing me with the wonderful opportunity to pursue my PhD degree. I am grateful to them for their continuous encouragement, support and guidance throughout of years of my study. I am thankful to the graduate supervisory committee overseeing my work, Dr. Tung Kum Hoe and Dr. Lee Wee Sun for their constructive suggestions and critical comments. Special thanks go to Dr. Wu Ping and Dr. Ankush Mittal for their guidance as well as helpful suggestions. Madam Leong Yoke Yee is also highly appreciated for helping me refine the thesis. I thank all past and present members of the computational biology lab for their idea sharing. The wonderful time we have spent together in NUS will be in my mind forever. My heartfelt appreciation goes to my beloved parents for their constant support and encouragement, without whom this would have remained but a dream. Finally, my deepest gratitude goes to my wife for her unconditional love, understanding and warm support through the years. ii

doi:10.1093/bioinformatics/bti406 Modularized learning of genetic interaction networks from biological annotations and mRNA expression data