請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/51240
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 魏志平(Chih-Ping Wei) | |
dc.contributor.author | Yu-Ting Chang | en |
dc.contributor.author | 張宇婷 | zh_TW |
dc.date.accessioned | 2021-06-15T13:28:17Z | - |
dc.date.available | 2021-02-24 | |
dc.date.copyright | 2016-02-24 | |
dc.date.issued | 2015 | |
dc.date.submitted | 2016-02-13 | |
dc.identifier.citation | Bisgin, H., Liu, Z., Kelly, R., Fang, H., Xu, X., & Tong, W. (2012). Investigating drug repositioning opportunities in FDA drug labels through topic modeling. BMC Bioinformatics, 13(Suppl 15), S6.
Cao, Z., Qin, T., Liu, T.-Y., Tsai, M.-F., & Li, H. (2007). Learning to rank: from pairwise approach to listwise approach. In Proceedings of the 24th International Conference on Machine Learning, 129-136. Chen, K.-A. (2013). Mining biomedical literature and ontologies for drug repositioning discovery. Unpublished Master Thesis, Graduate Institute of Information Management, National Taiwan University. Cilibrasi, R. L., & Vitanyi, P. (2007). The google similarity distance. IEEE Transactions on Knowledge and Data Engineering, 19(3), 370-383. Emig, D., Ivliev, A., Pustovalova, O., Lancashire, L., Bureeva, S., Nikolsky, Y., & Bessarabova, M. (2013). Drug target prediction and repositioning using an integrated network-based approach. PLoS One, 8(4), e60618. Gottlieb, A., Stein, G. Y., Ruppin, E., & Sharan, R. (2011). PREDICT: a method for inferring novel drug indications with application to personalized medicine. Molecular Systems Biology, 7(1), 496. He, B., Tang, J., Ding, Y., Wang, H., Sun, Y., Shin, J. H., . . . Desai, P. (2011). Mining relational paths in integrated biomedical data. PLoS One, 6(12), e27506. Joachims, T. (2006). Training linear SVMs in linear time. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 217-226. Kuhn, M., Campillos, M., Letunic, I., Jensen, L. J., & Bork, P. (2010). A side effect resource to capture phenotypic effects of drugs. Molecular Systems Biology, 6(1), 343. Lee, S., Choi, J., Park, K., Song, M., & Lee, D. (2012). Discovering context-specific relationships from biological literature by using multi-level context terms. BMC Medical Informatics and Decision Making, 12(Suppl 1), S1. Lenz, W., Pfeiffer, R. A., Kosenow, W., & Hayman, D. (1962). Thalidomide and congenital abnormalities. The Lancet, 279(7219), 45-46. Li, J., Zhu, X., & Chen, J. Y. (2009). Building disease-specific drug-protein connectivity maps from molecular interaction networks and PubMed abstracts. PLoS Computational Biology, 5(7), e1000450. Lu, J., & Lu. Z. (2012). A new method for computational drug repositioning using drug pairwise similarity. In Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 1-4. Napolitano, F., Zhao, Y., Moreira, V. M., Tagliaferri, R., Kere, J., D'Amato, M., & Greco, D. (2013). Drug repositioning: a machine-learning approach through data integration. Journal of Cheminformatics, 5, 30. Özgür, A., Vu, T., Erkan, G., & Radev, D. R. (2008). Identifying gene-disease associations using centrality on a literature mined gene-interaction network. Bioinformatics, 24(13), i277-i285. Paolini, G. V., Shapland, R. H., van Hoorn, W. P., Mason, J. S., & Hopkins, A. L. (2006). Global mapping of pharmacological space. Nature biotechnology, 24(7), 805-815. Swanson, D. R. (1986). Fish oil, Raynaud's syndrome, and undiscovered public knowledge. Perspectives in biology and medicine, 30(1), 7-18. Swanson, D. R., & Smalheiser, N. R. (1997). An interactive system for finding complementary literatures: a stimulus to scientific discovery. Artificial Intelligence, 91(2), 183-203. Vos, R. (2012). Drugs Looking for Diseases: Innovative Drug Research and the Development of the Beta Blockers and the Calcium Antagonists (Vol. 120): Springer Science & Business Media. Wang, Y., Xiao, J., Suzek, T. O., Zhang, J., Wang, J., & Bryant, S. H. (2009). PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Research, 37(suppl 2), W623-W633. Weeber, M., Klein, H., de Jong‐van den Berg, L., & Vos, R. (2001). Using concepts in literature‐based discovery: Simulating Swanson's Raynaud–fish oil and migraine–magnesium discoveries. Journal of the American Society for Information Science and Technology, 52(7), 548-557. Wishart, D. S., Knox, C., Guo, A. C., Cheng, D., Shrivastava, S., Tzur, D., Gautam, B. & Hassanali, M. (2008). DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Research, 36(suppl 1), D901-D906. Wren, J. D., Bekeredjian, R., Stewart, J. A., Shohet, R. V., & Garner, H. R. (2004). Knowledge discovery by automated identification and ranking of implicit relationships. Bioinformatics, 20(3), 389-398. Wu, C., Gudivada, R. C., Aronow, B. J., & Jegga, A. G. (2013). Computational drug repositioning through heterogeneous network clustering. BMC Systems Biology, 7(Suppl 5), S6. Xia, F., Liu, T.-Y., Wang, J., Zhang, W., & Li, H. (2008). Listwise approach to learning to rank: theory and algorithm. In Proceedings of the 25th International Conference on Machine Learning, 1192-1199. Yetisgen-Yildiz, M., & Pratt, W. (2006). Using statistical and knowledge-based approaches for literature-based discovery. Journal of Biomedical Informatics, 39(6), 600-611. Yetisgen-Yildiz, M., & Pratt, W. (2009). A new evaluation methodology for literature-based discovery systems. Journal of Biomedical Informatics, 42(4), 633-643. Zhang, P., Agarwal, P., & Obradovic, Z. (2013). Computational drug repositioning by ranking and integrating multiple data sources Machine Learning and Knowledge Discovery in Databases (pp. 579-594): Springer. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/51240 | - |
dc.description.abstract | 藥物開發過程繁複,其成本高且成功率低。根據美國食品藥品管理局(FDA)規定,新藥物需通過動物、人體實驗、委員會審核等七大流程才可以在市場中販售。然而,只要其中一個流程無法通過,此開發工程就前功盡棄,開發藥廠將承受所有的損失。因此,藥物開發人員開始引入「舊藥新用」的開發方式,從既有藥物中,在其原本設計標的外,尋找新適應症。若能從將既有藥物重新定位、找到新用途,開發藥廠就可因此減少開發的時間和成本。
Swanson (1986)最先提出以醫學文獻探勘實現藥物重新定位的技術,而Chen (2013)基於Swanson的ABC模型,進行改良並納入醫藥知識庫,提出Path模型。本研究結合了醫學文獻及醫藥知識庫,使用ABC和Path模型以不同排序演算法產生的藥物對適應症之排序結果作為特徵,以排序學習(Learning to Rank)中的文檔列表方法(List-wise Approach)構建藥物重新定位系統–DRLTR。 我們以各個特徵及其他排序學習方法對DRLTR進行比較性實驗,結果顯示DRLTR都有顯著的進步,表示我們所提出的方法可以更有效地提供潛在的藥物及疾病關係給研究者,以協助研究人員進行藥物的重新定位。 | zh_TW |
dc.description.abstract | Drug development is costly and time consuming. According to the regulation of United States Food and Drug Administration (FDA), drug development consists of eight stages, including animal and human test, application review, etc. However, once one of the stages fails, the investment on candidate drug seldom returns. Therefore, drug developers and researchers start to explore the drug repositioning approach for drug development. If they can successfully find novel indications for an existing drug, the pharmaceutical company could save a lot of time and money.
Swanson (1986) first proposed a drug repositioning technique by mining biomedical literature database. Chen (2013) refined Swanson’s ABC Model and considered ontologies into his Path Model. In this study, we combine both literature- and ontology-based databases, apply the ABC and Path Model with different ranking algorithms to generate several ranking scores as our features, and then propose a Drug Repositioning Learning-to-Rank System (DRLTR) that employs the list-wise learning-to-rank approach for effectively determining the final ranking. In our systematic evaluation experiments, we take each type of features and other learning to rank methods to compare with our proposed DRLTR system. As our experiment results show, DRLTR can effectively predict potential drug-disease relationships for drug developers and help them on the research of drug repositioning. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T13:28:17Z (GMT). No. of bitstreams: 1 ntu-104-R02725025-1.pdf: 774537 bytes, checksum: 62d67f311d0bffcd94e2dfea791428ae (MD5) Previous issue date: 2015 | en |
dc.description.tableofcontents | 謝辭 ii
中文摘要 iii Abstract iv List of Contents v List of Figures vii List of Tables viii Chapter 1 Introduction 1 1.1 Background 1 1.2 Research Motivation and Objective 3 Chapter 2 Literature Review 6 2.1 Literature-based Discovery 6 2.2 Ontology-based Discovery 8 2.3 Integration of Literature and Ontology 10 Chapter 3 Our Proposed Drug Repositioning System 11 3.1 Concept Network Construction 12 3.1.1 Literature-based Concept Network Construction 12 3.1.2 Ontology-based Concept Network Construction 13 3.2 Learning System 14 3.2.1 Training Instances 14 3.2.2 Feature Extraction 15 3.2.3 Summary of Features 18 3.2.4 Ranking Model Building 20 3.3 Detecting System 22 3.4 Additional Measures Calculation 22 3.4.1 Measure Calculation 22 3.4.2 Drug-disease Aggregation 24 Chapter 4 Evaluation and Results 26 4.1 Evaluation Design 26 4.2 Tuning Experiments 28 4.3 Comparative Experiments 32 4.3.1 Comparing DRLTR with the best benchmarks 32 4.3.2 Comparing DRLTR with existing ontology-based methods 33 4.4 In-depth Analysis 35 4.4.1 Effects of feature selection 35 4.4.2 Effects of different types of features 36 4.4.3 Effects of different learning to rank methods 37 Chapter 5 Conclusion and Future Work 38 References 40 | |
dc.language.iso | en | |
dc.title | 以排序學習方法整合多元資訊實現藥物重新定位 | zh_TW |
dc.title | Computational Drug Repositioning: A Learning to Rank Approach with Multiple Data Sources | en |
dc.type | Thesis | |
dc.date.schoolyear | 104-1 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 楊錦生(Chin-Sheng Yang),蕭斐元(Fei-Yuan Hsiao),李彥賢(Yen-hsien Lee) | |
dc.subject.keyword | 舊藥新用,醫學文獻探勘,監督式學習,排序學習,文檔列表方法, | zh_TW |
dc.subject.keyword | drug repositioning,biomedical literature mining,supervised learning,learning to rank,list-wise approach, | en |
dc.relation.page | 42 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2016-02-13 | |
dc.contributor.author-college | 管理學院 | zh_TW |
dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
顯示於系所單位: | 資訊管理學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-104-1.pdf 目前未授權公開取用 | 756.38 kB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。