Construction of Continuous-State Bayesian Networks Using D-Separation Property and Partial Correlations

• Main Authors: Chia-Yi Chien, 簡佳怡
• Other Authors: 陳正剛
• Format: Others
• Language: en_US
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/71625418714253282137

碩士 === 國立臺灣大學 === 工業工程學研究所 === 94 === The development of microarray technology is capable of generating a huge amount of gene expression data at once to help us analyze the whole genome mechanism. Many analysis methods have been developed and applied to analyze the microarray data, such as Clustering analysis, Factor analysis and Bayesian networks. Bayesian networks can better help biologists to understand the biological meanings behind the microarray data. In general, algorithms of Bayesian network construction can be divided into two categories: the search-and-score approach and the constraint-based approach. How to construct Bayesian networks rapidly and efficiently become a challenge to biotechnology researches. Before constructing a Bayesian network, the node ordering is the first difficulty and the actual node ordering is usually unknown. In this research, we develop a method to search for possible node orderings based on the d-separation property. There are three assigning procedures in the node ordering algorithm. With the proposed ordering procedures, we produce three possible node sequences. We also propose an algorithm of Bayesian network construction by using d-separation property and partial correlation to analyze variables with continuous states. Our algorithm is one of to the constraint-based approaches. Finally, we apply our algorithm to two real-word cases; one is the Saccharomyces cerevisiae cell cycle gene expression data collected by Spellman et al., and the other is the caspases data.