Construction of Continuous-State Bayesian Networks Using D-Separation Property and Partial Correlations
Date Issued
2006
Date
2006
Author(s)
Chien, Chia-Yi
DOI
en-US
Abstract
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.
Subjects
貝氏網路
淨相關
節點順序
連
續變數
Bayesian network
d-separation
partial correlation
node ordering
continuous states
Type
thesis
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