We present here LOCATE, a curated, web-accessible database that houses data describing the membrane organization and subcellular localization of proteins from the FANTOM3 Isoform Protein Sequence set. Membrane organization is predicted by the highthroughput, computational pipeline MemO. The subcellular locations of selected proteins from this set were determined by a high-throughput, immunofluorescence-based assay and by manually reviewing.1700 peer-reviewed publications. LOCATE represents the first effort to catalogue the experimentally verified subcellular location and membrane organization of mammalian proteins using a high-throughput approach and provides localization data for 40 % of the mouse proteome. It is available at

The complete genome sequences have provided a plethora of potential drug targets. Gene network technique holds the promise of providing a conceptual framework for analysis of the profusion of biological data being generated on potential drug targets and providing insights to understand the biological regulatory mechanisms in diseases, which are playing an increasingly important role in searching for novel drug targets from the information contained in genomics. In this paper, we discuss some of the network-based approaches for identifying drug targets, with the emphasis on the gene network strategy. In addition, some of the relevant data resources and computational tools are given. 1

Exploring gene regulatory network is a key topic in molecular biology. In this paper, we present a new dynamic Bayesian network (DBN) framework embedded with structural expectation maximization (SEM) to model gene relationship. It is well-suited for analyzing the time-series data and can deal with cyclical structures that can not be tackled by static Bayesian network. We applied the new method to learning the regulatory network and the metabolic pathway from Saccharomyces Cerevisiae cell cycle gene expression data. The results show that the proposed method is capable of handling missing values in expression data sets, and the inference accuracy can further be improved.

A dynamic Bayesian network (DBN) is a probabilistic network that models interdependent entities that change over time. Given example sequences of multivariate data, we use a genetic algorithm to synthesize a network structure that models the causal relationships that explain the sequence. We use a multi-objective evaluation strategy with a genetic algorithm. The multi-objective criteria are a network’s probabilistic score and structural complexity score. Our use of Pareto ranking is ideal for this application, because it naturally balances the effect of the likelihood and structural simplicity terms used in the BIC network evaluation heuristic. We use a simple structural scoring formula, which tries to keep the number of links in the network approximately equivalent to the number of variables. We also use a simple representation that favours sparsely connected networks similar in structure to those modeling biological phenomenon. Our experiments show promising results when evolving networks ranging from 10 to 30 variables, using a maximal connectivity of between 3 and 4 parents per node. The results from the multi-objective GA were superior to those obtained with a single objective GA. Key words: dynamic Bayesian networks, multi-objective optimization, genetic algorithms

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

LOCATE: a mouse protein subcellular localization database