經由潛在語義的線索從蛋白質交互作用網路進行蛋白質功能的預測

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姓名

林冠宏(Guan-Hong Lin) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

經由潛在語義的線索從蛋白質交互作用網路進行蛋白質功能的預測
(Protein Function Prediction from Protein Interaction Networks by Latent Semantic Indexing)

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摘要(中)

了解各種蛋白質在細胞中的作用一直是生物學中一項很重要的課題，近年來，由於新的實驗技術相繼問世，有些實驗技術可以在單一實驗中產生大量實驗結果，例如雙雜合系統可以在一次實驗中產生大量蛋白質交互作用的資料，這些資料通常都會隱含著某些具有生物意義的訊息。
在這篇論文中，我們提出了一個基於潛在語義的線索的方法，這個方法可以用來萃取隱藏在蛋白質交互作用網路中具有生物意義的訊息。在資訊擷取的領域中，一字多義與多字一義一直是導致擷取結果不正確的主因，而潛在語義的線索具有解決這些問題的能力。在蛋白質交互作用網路中，經常會存在一些錯誤或者是不明確的訊息，我們利用潛在語義的線索來過濾這一些訊息。我們的結果顯示出這個方法確實能幫我們過濾這些訊息並且擷取出具有高度功能相關的蛋白質。

摘要(英)

Determining protein function is one of the most important tasks in the post-genomic era. Large-scale biological experiment results such as protein interaction networks can be obtained now, and these data often involve the information about protein functions.
In this thesis, we present an approach based on Latent Semantic Indexing (LSI) to extract this information from protein interaction networks. LSI is an information retrieval technique that can solve the synonymy and polysemy problems. Because biologists believe that there are a lot of false positives and false negatives in protein interaction networks, we use the properties of LSI to filter out the wrong and confused information retrieved from these networks. Our results show that our approach can find out the functional related proteins in cells.

關鍵字(中)

★ 蛋白質功能預測
★ 蛋白質交互作用網路
★ 潛在語義的線索

關鍵字(英)

★ protein interaction network
★ protein function prediction
★ latent semantic indexing

論文目次

TABLE OF CONTENTS I
LIST OF FIGURES II
LIST OF TABLES III
1 INTRODUCTION 1
2 RELATED WORK 5
2.1 Methods based on Sequence Similarity 5
2.2 Methods based on Biological Experiment Data 7
2.3 Comparison between These Directions 10
3 MATERIALS AND METHODS 12
3.1 Introduction of Latent Semantic Indexing 13
3.1.1 Term-Document Matrix 13
3.1.2 Truncated Singular Value Decomposition 14
3.1.3 Similarity Definition 15
3.1.4 Basic Properties of LSI 16
3.2 Latent Semantic Indexing of Protein Interaction Network 17
3.2.1 Modeling 17
3.2.2 Similarity and Clustering 18
4 EXPERIMENTS, RESULTS, AND DISCUSSIONS 20
4.1 Data Handling 20
4.2 Validations of Our Method 21
4.2.1 Experiment processes 21
4.2.2 Similarity Test Results 22
4.2.3 Clustering Test Results 28
4.3 Fault Tolerance Experiments 33
4.3.1 Experiment Process 33
4.3.2 Fault Tolerance Results 33
4.4 Comparison with Other Methods 36
5 CONCLUSION AND FUTURE WORK 39
REFERENCE 40

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指導教授

何錦文(Chin-Wen Ho)

審核日期

2005-7-18

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