臺灣博碩士論文加值系統

由於生物晶片技術發展，一次的實驗即可提供我們大量的基因表現資料，利用這些資料可以幫助我們了解整個基因機制。目前已經發展許多方法用來分析生物晶片的資料，例如：集群分析法、因子分析法與貝氏網路。貝氏網路可以幫助生物學家了解更多潛藏在生物晶片資料後的生物資訊。一般來說，建立貝氏網路的方法可以分為兩類：搜尋與分數方法(search-and-score)和條件限制方法(constraint-based). 如何快速有效地建立基因網路對生物技術方法的研究者來說是一個極大的挑戰。
在建立基因網路之前，第一個會遇到的問題就是節點的先後順序，而且現實生活中真正的順序通常是不知道的。所以在本研究中，我們發展一個方法用來尋找可能的節點順序藉由d-separation的性質，我們的方法包含三種分配節點的作法，當應用我們的方法來可以尋找到三種可能的節點順序。我們也提出一個建立基因網路的方法藉由d-separation的性質與淨相關來分析連續變數。我們的方法是屬於條件限制的方法。最後，我們運用提出的方法來分析兩個實際的例子； 一個是酵母菌細胞週期的資料，另外一個是細胞凋亡的資料。

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.

Contents
Contents.................................................................................................................v
Contents of Figures................................................................................................ii
Contents of Tables..................................................................................................v
Chapter 1: Introduction..................................................................................................1
1.1 Background......................................................................................................1
1.2 Definitions of Bayesian Networks and Approaches of Bayesian Network Construction....................................................................................................3
1.2.1 Definitions of Bayesian Networks........................................................4
1.2.2 Concepts of Search and Score Approaches...........................................6
1.2.3 Concepts of Constraint-Based Approach..............................................7
1.2.4 The Summary of Constraint-Based Algorithms..................................11
1.3 Research Objectives.......................................................................................13
1.4 Thesis Organization.......................................................................................14
Chapter 2: Bayesian Network Construction Using D-Separation Property and Partial Correlation...................................................................................................................15
2.1 The Concepts of Blocking Effect and Time-Shift..........................................15
2.2 Node Ordering Algorithm..............................................................................19
2.3 Forward Algorithm for Bayesian Network Construction...............................28
2.4 Illustration with a Simple Example................................................................31
Chapter 3: Case Study..................................................................................................33
3.1 The Chest-clinic network...............................................................................33
3.2 Saccharomyces cell cycle gene expression dataset........................................36
3.3 Caspases dataset.............................................................................................40
Chapter 4: Conclusion and Future Research................................................................45
References....................................................................................................................46

1
Anderverg, M. (1973). Cluster Analysis for Applications. Academic Presss.
2
Michael S. Lewis-Beck(c1994). Factor analysis and related techniques, London : Sage Publications.
3
Druzdzel, M. J. and R. R. Flynn (1999). Decision Support Systems. Encyclopedia of Library and Information Science. A. Kent, Marcel Dekker, Inc.
4
Beinlich et a[., 19891 Beinlich, I.A., Suermondt, H.J., Chavg R.M. and Cooper, G.F., The ALARM monitoring system: A case study with two probabilistic inference techniques for belief networks. Proceedings of the Second European Confirence on Artificial Infeliigence in Medicine @p.247-256) London, England, 1989.
5
Friedman, L.K., Ginsberg, M.D., Belayev, L., Busto, R., Alonso, O.F., Lin, B., Globus, M.Y., 2001. Intraischemic but not postischemic hypothermia prevents non-selective hippocampal downregulation of AMPA and NMDA receptor gene expression after global ischemia. Brain Res Mol Brain Res, 86(1-2):34-47.
6
Heckerman, D. Geiger, D. and Chickering, D.M. Learning Bayesian networks: the combination of knowledge and statistical data. Machine Learning, 20,131-163, 1995.
7
Schwarz, G. (1978). “Estimating the Dimension of a Model.” The Annals of Statistics, 6, 2, 461-464.
8
[Rissanen, 1978] J. Rissanen. Modeling by shortest data description. Automatica, 14:445–471, 1978.
9
[Rissanen, 1987] J. Rissanen. Stochastic complexity. Journal of the Royal Statistical Society, 49(3):223–239 and 252–265, 1987.
10
Akaike, H. (1974). A new look at statistical model identification. IEEE Transactions on Automatic Control, AC–19, 716–723.
11
[Cooper and Herskovits, 1992] Gregory F. Cooper and Edward Herskovits. A Bayesian method for the induction of probabilistic networks from data.
Machine Learning, 9(4):309{347, 1992.
12
P. Spirtes, C. Glymour, R. Scheines, (2000), Prediction Causation and Search, 2nd Ed., MIT Press.
13
Wermuth, N., Lauritzen, S.L., 1983. Graphical and recursive models for contingency tables. Biometrika, 70(3), 537-552.
14
KISHINO, H., WADDELL, P. (2000). Correspondence Analysis of Genes
and Tissue Types and Finding Genetic Links from Microarray Data. Gneome
Informatics 11: 83-95.
15
T. Chu, C. Glymour, R. Scheines and P. Spirtes, (2002) A Note on a
Statistical Problem for Inference to Gene Regulation from Microarray
Data, Bioinformatics, in press.
16
Magwene PM and Kim J (2004) Estimating genomic coexpression using first order conditional independence. Genome Biology 5, R100
17
Jie Cheng , Russell Greiner , Jonathan Kelly , David Bell , Weiru Liu, Learning Bayesian networks from data: an information-theory based approach, Artificial Intelligence, v.137 n.1-2, p.43-90, May 2002
18
Cooper, G. F. and E. Herskovits (1992). A Bayesian method for the induction of probabilistic networks from data. Machine Learning 9, 309–347.
19
Jie Cheng and David Bell, Liu Weiru. Learning Bayesian Networks from Data: An Efficient Approach Based on Information Theory
20
Acid, S. and Campos, L.M., An algorithm for finding minimum d-Separating sets in belief networks, Proceedings of the twelfth Conference of Uncertainty inArtificial Intelligence, 1996.