臺灣博碩士論文加值系統

過去有關重建生物反應網路的研究指出基於網路的方法可以顯示特定物種的基因和基因產物的關係。在本研究裡我們使用大腸桿菌K-12 MG1655這個重要的模式生物作為重建生物反應網路的特定物種。有關大腸桿菌的生物反應資料通常可在資料庫以及文獻裡取得；但在資料庫裡大多數的生物反應都缺少詳細的文獻註解；而且要單純透過人工閱讀大量的文獻來獲取生物反應的註解也是相當的困難。
因此我們使用文獻探勘(text mining)的方法從PubMed資料庫裡310,378篇有關大腸桿菌的文獻摘要來自動提取生物反應並且提供句子描述生物反應如何發生的詳細註解。我們的文獻探勘方法在隨機抽樣的評估結果中在辨識基因調控(gene regulations)、相互作用(physical interactions)以及訊號傳遞(signal transductions)這三種生物反應的F-score值可達到0.81、0.86和0.93。經由文獻探勘提取的結果，再以人工驗證文獻的內容確，最後確認出1,084個生物反應;其中394個生物反應是我們從資料庫所蒐集到的生物反應所沒包含的。這394個新找的生物反應可對目前大腸桿菌的生物反應網路提供新的觀點以及可連接生物反應網路裡的空隙。文獻探勘的方法在辨識生物反應的準確度(precision)可達52%，而且提供句子描述的詳細註解給12% 從資料庫所蒐集到的生物反應。
我們對文獻探勘新找的生物反應所參與的基因進行功能顯著分析(functional enrichment analysis)後發現顯著的功能分類(functional category)是DNA的複製和修復、生物膜的形成及細胞運動並且和RpoS基因主導的應激反應(stress response)有關。
結合從資料庫蒐集以及文獻探勘提取的生物反應可重建大腸桿菌新的整合式生物反應網路。在整合式生物反應網路裡，新找到的生物反應可連結從資料庫蒐集的生物反應產生的離散子網路，而且也讓基因調控網路的階層結構產生變化。

Previous studies of reconstructing interaction networks indicated that network-based methods represent the relationships between genes and gene products of the target organisms. In this study we used E. coli K-12 MG1655, an important model organism, as the target organism for reconstructing interaction networks. Interactions of E. coli large molecules are usually obtained from databases and literature. Most interactions in the databases are lacking of literature supports. It is challenging to retrieve traceable literature citations for these interactions of E. coli manually.
We applied text mining methods to extract interactions from 310,378 abstracts of E. coli researches in PubMed databases and provide sentence-level annotations of the interactions. F-scores of 0.81, 0.86, and 0.93 were achieved for identification of gene regulations, physical interactions and signal transductions by text mining in random sampling evaluations. 1,084 interactions were identified after text mining extraction. We found that 394 of the 1,084 interactions were newly identified interactions comparing to collected interactions from the E. coli databases. These 394 newly identified interactions provided new insights and bridged the gaps in the interaction networks of E. coli. The precision of 52% was achieved for the identifications of interactions through text mining. We provided sentence-level annotations for 12% of collected interactions in the E. coli databases.
We performed functional enrichment analysis of the genes involved in the newly identified interaction extracted by text mining. The enriched functional categories are DNA replication and repair, biofilm formation, and cell motility associated with RpoS-centered stress responses of E. coli.
After combing interactions collected from the databases and extracted through text mining, we reconstructed integrated networks of E. coli. From the integrated networks, we found that the newly identified interactions filled the gaps between separated components of the interaction networks based on collected interactions from the databases. The newly identified interactions also led to the organizational changes of hierarchical structure of E. coli’s gene regulatory networks.

ACKNOWLEDGMENTS I
中文摘要 II
ABSTRACT IV
CONTENTS VI
LIST OF TABLES IX
LIST OF FIGURES X
CHAPTER 1. INTRODUCTION 1
1.1 Biological interactions, biological networks and integrated networks 1
1.2 How to reconstruct biological interaction networks? 1
1.3 Current interaction collections and interaction networks of E coli 2
1.4 The use of text mining in reconstructing the interaction networks of E. coli 4
1.5 Goal and specific aims 5
CHAPTER 2. MATERIALS AND METHODS 8
2.1 Retrieve abstracts of published researches on E. coli 8
2.2 Recognize names of genes and gene products in the collected abstracts 8
2.3 Extract interactions from the sentences of the collected abstracts 10
2.4 Comparison of interactions extracted by text mining with interactions collected from databases 14
2.5 Functional enrichment analysis 14
CHAPTER 3. RESULTS 16
3.1 F-scores achieved for identifications of GRs, PIs and STs through text mining 16
3.2 The interactions correctly identified by text mining 16
3.3 The interactions extracted by text mining that were found and not found in the collected E. coli databases 16
3.4 Genes involved in text mining extracted interactions are enriched in transcription, DNA replication and signal transduction 21
3.5 Genes involved in the new interactions were more enriched in DNA replication and repair, biofilm formation, cell motility than genes involved in the total interactions extracted by text mining 27
3.6 The integrated networks of E. coli reconstructed by interactions derived from databases and text mining 29
CHAPTER 4. DISCUSSIONS 32
4.1 SRL is effective to identify PIs, and DKF is effective to identify STs 32
4.2 Limitations on interaction discoveries based on text mining 33
4.3 The organization of gene regulatory networks of E. coli may not be hierarchical 34
4.4 New interactions brought new biological insights on the stress responses in E. coli 35
CHAPTER 5. CONCLUSIONS 37
5.1 Next-generation text mining for reconstructions of biological interactions 37
5.2 New discoveries of E. coli’s interaction networks based on text mining 37
Future works 38
REFERENCE 40
TABLES 50
FIGURES 57

LIST OF TABLES
Table 1. The number of abstracts, sentences and entity pairs of training corpuses for machine learning based scoring models. 50
Table 2. Evaluation results of four text mining strategies for identifications of GRs, PIs and STs based on randomly samplings. 51
Table 3. The data sources and quantities of E. coli’s interactions collected from the databases. 52
Table 4. The results of manual curation for the interactions extracted by “MLS with SRL and DKF” text mining strategy. 53
Table 5. The enriched COG functional categories in the newly identified interactions extracted by text mining. 54
Table 6. The enriched MultiFun (level 2) functional categories in the newly identified interactions extracted by text mining. 55
Table 7. The enriched KO (level 2) functional categories in the newly identified interactions extracted by text mining. 56

LIST OF FIGURES
Figure 1. The growth of curated articles in EcoCyc database and articles associated with E. coli archived in PubMed database (2004 - 2013). 57
Figure 2. The workflow of text mining based interaction extraction. 58
Figure 3. The numbers of abstracts, entity pairs and entities of each step in the workflow based on 301,378 PubMed abstracts. 60
Figure 4. The comparisons of F-scores, precisions and recalls for four text mining strategies in random sampling evaluations. 62
Figure 5. The comparison of compositions of the 394 new interactions, 690 known interactions, and total 1,084 interactions extracted by text mining. 63
Figure 6. The recovered degradation pathway of sigma factor 38, RpoS based on the newly identified ST under oxidative stress. 64
Figure 7. The precisions of “MLS with SRL and DKF” text mining strategy to identify 1,084 total interactions and 690 known interactions. 65
Figure 8. Example of a sentence-level annotation for a gene regulation between ompR and ompC in RegulonDB database. 66
Figure 9. Recoveries of the interactions and the key molecules by “MLS with SRL and DKF” and “MLS” text mining strategies. 67
Figure 10. The compositions of interactions within the five enriched COG functional categories. 69
Figure 11. The compositions of interactions within the five enriched MultiFun (level 1) functional categories. 70
Figure 12. The compositions of interactions within the twenty enriched MultiFun (level 2) functional categories. 71
Figure 13. The compositions of interactions within the seven enriched KO (level 2) functional categories. 73
Figure 14. Integrated networks reconstructed by the interactions derived from the databases and text mining. 74
Figure 15. Networks reconstructed by the interactions collected from the databases. 76
Figure 16. Reductions and generations of connected components in the networks derived from the databases by introducing newly identified interactions based on text mining. 78
Figure 17. The organizational change of E. coli’s gene regulatory networks layers decreased after we introducing 79 new GRs extracted by text mining. 80
Figure 18. The numbers of genes in each hierarchical layer of E. coli’s gene regulatory networks with and without newly identified GRs extracted by text mining. 83
Figure 19. An rpoS-centered network generated by interactions collected from the databases and extracted by text mining. 84

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