Abstract not found

We propose a new statistical method for constructing a genetic network from microarray gene expression data by using a Bayesian network. An essential point of Bayesian network construction is in the estimation of the conditional distribution of each random variable. We consider fitting nonparametric regression models with heterogeneous error variances to the microarray gene expression data to capture the nonlinear structures between genes. A problem still remains to be solved in selecting an optimal graph, which gives the best representation of the system among genes. We theoretically derive a new graph selection criterion from Bayes approach in general situations. The proposed method includes previous methods based on Bayesian networks. We demonstrate the effectiveness of the proposed method through the analysis of Saccharomyces cerevisiae gene expression data newly obtained by disrupting 100 genes. 1.

We propose a statistical method based on graphical Gaussian models for estimating large gene networks from DNA microarray data. In estimating large gene networks, the number of genes is larger than the number of samples, we need to consider some restrictions for model building. We propose weighted lasso estimation for the graphical Gaussian models as a model of large gene networks. In the proposed method, the structural learning for gene networks is equivalent to the selection of the regularization parameters included in the weighted lasso estimation. We investigate this problem from a Bayes approach and derive an empirical Bayesian information criterion for choosing them. Unlike Bayesian network approach, our method can find the optimal network structure and does not require to use heuristic structural learning algorithm. We conduct Monte Carlo simulation to show the effectiveness of the proposed method. We also analyze Arabidopsis thaliana microarray data and estimate gene networks.

We propose a new method for identifying and validating drug targets by using gene networks, which are estimated from cDNA microarray gene expression profile data. We created novel gene disruption and drug response microarray gene expression profile data libraries for the purpose of drug target elucidation. We use two types of microarray gene expression profile data for estimating gene networks and then identifying drug targets. The estimated gene networks play an essential role in understanding drug response data and this information is unattainable from clustering methods, which are the standard for gene expression analysis. In the construction of gene networks, we use the Bayesian network model. We use an actual example from analysis of the Saccharomyces cerevisiae gene expression profile data to express a concrete strategy for the application of gene network information to drug discovery.

The pdf and the html versions of this paper (and related ones) are available from

We present a graphical criterion for reading dependencies from the minimal directed independence map G of a graphoid p, under the assumption that G is a polytree and p satisfies weak transitivity. We prove that the criterion is sound and complete. We argue that assuming weak transitivity is not too restrictive. 1

Inflammation is an inherent response of the organism that permits its survival despite constant environmental challenges. The process normally leads to recovery from injury and to healing. However, if targeted destruction and assisted repair are not properly phased, chronic inflammation can result in persistent tissue damage. To better understand the inflammatory process, we recently introduced a profiling methodology to identify common genes involved in bladder inflammation. The method represents a complementation to the classical quantification of inflammation and provides information regarding the early, intermediate, and late events in gene regulation. However, geneprofiling fails to describe the molecular pathways and their interconnections involved in the particular inflammatory response. The present work introduces a new statistic technique for inferring functional interconnections between inflammatory pathways underlying classical models of bladder inflammation and permits the modeling of the inflammatory network. This new statistical

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

We present a probabilistic modeling approach to learning gene transcriptional regulation networks from time series gene expression data that is appropriate for the sparsely and irregularly sampled time series datasets currently available. We use a clustering algorithm based on statistical splines to estimate continuous probabilistic models for clusters of genes with similar time expression profiles and for individual genes. Using the learned models, we present a novel mutual information score for causal edges between pairs of clusters and between pairs of genes corresponding to a given time lag #. This score computes dependency between expression values as continuous quantities rather than discretizing them. We present empirical results on times series data for the yeast cell cycle, using randomization trials to determine statistically significant candidate network edges and the Chow-Liu graph learning algorithm to learn the network structure, to obtain a dynamic model of cell cycle regulation. Biological validation of the inferred network suggests that our method can learn a meaningful, higher-level view of regulatory networks from sparse time series data.

Introduction Recently, we have developed a system, named "ASIAN", for inferring a network by the combination of clustering and graphical Gaussian modeling (GGM) [1, 2, 3, 4, 5]. The feasibility of the system was validated by the application of the system to the data of gene expression profiles. The system is composed of five parts: the calculation of correlation coe#cient matrix of raw data, the hierarchical clustering, the determination of cluster boundaries, the calculation of the average data from raw data based on the determined cluster number, and the application of GGM to the average data. Since the system was designed to apply the gene expression profiles, the first four parts were prerequisite to analyze the redundant data including many similar patterns of expression profiles by the last part, application of GGM. The five programs were performed in turn in the above order, being operated from the command line in UNIX machine. In this study, we present a web site [6] to util