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DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 魏志平 | |
dc.contributor.author | Yun-Jing Chen | en |
dc.contributor.author | 陳昀靖 | zh_TW |
dc.date.accessioned | 2021-06-17T08:12:46Z | - |
dc.date.available | 2021-08-19 | |
dc.date.copyright | 2019-08-19 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019-08-15 | |
dc.identifier.citation | Ashburn, T. T., & Thor, K. B. (2004). Drug repositioning: Identifying and developing new uses for existing drugs. Nature Reviews Drug Discovery, 3(8), 673–683.
Boolell, M., Allen, M. J., Ballard, S. A., Gepi-Attee, S., Muirhead, G. J., Naylor, A. M., … Gingell, C. (1996). Sildenafil: an orally active type 5 cyclic GMP-specific phosphodiesterase inhibitor for the treatment of penile erectile dysfunction. International Journal of Impotence Research, 8(2), 47—52. Bordes, A., Usunier, N., Weston, J., & Yakhnenko, O. (2013). TransE: Translating Embeddings for Modeling Multi-Relational Data. Advances in NIPS, 26, 2787–2795. Bordes, A., & Weston, J. (2011). Learning Structured Embeddings of Knowledge Bases. (Twenty-Fifth AAAI Conference on Artificial Intelligence.), 301–306. Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, ACM, 785–794. Densen, P. (2011). Challenges and opportunities facing medical education. Transactions of the American Clinical and Climatological Association, 122(319), 48–58. Kazama, J., Makino, T., Ohta, Y., & Tsujii, J. (2002). Tuning support vector machines for biomedical named entity recognition. (July), 1–8. Kilicoglu, H., Fiszman, M., Rodriguez, A., Shin, D., M, A., & Rindflesch, T. C. (2008). Semantic MEDLINE : A Web Application for Managing the Results of PubMed Searches Concordia University , Department of Computer Science and Software Engineering , Mont-. Genetics. Lee, K. (2017). Literature-based Discovery for Drug Repurposing: A path-importance-based Approach. (July). Lin, Y., Liu, Z., Sun, M., Lin, Y., & Zhu, X. (2015). TransR: Learning Entity and Relation Embeddings for Knowledge Graph Completion. Twenty-Ninth AAAI Conference on Artificial Intelligence. https://doi.org/10.1016/j.procs.2017.05.045 Liu, Y., Bill, R., Fiszman, M., Rindflesch, T., Pedersen, T., Melton, G. B., & Pakhomov, S. V. (2012). Using SemRep to label semantic relations extracted from clinical text. AMIA ... Annual Symposium Proceedings. AMIA Symposium, 2012, 587–595. Retrieved from Pammolli, F., Magazzini, L., & Riccaboni, M. (2011). The productivity crisis in pharmaceutical R&D. Nature Reviews Drug Discovery, 10, 428. Price, V. H. (1999). Treatment of Hair Loss. The New England Journal of Medicine, 2–4. Rastegar-Mojarad, M., Elayavilli, R. K., Li, D., Prasad, R., & Liu, H. (2015). A new method for prioritizing drug repositioning candidates extracted by literature-based discovery. Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015, 669–674. Rastegar-Mojarad, M., Elayavilli, R. K., Wang, L., Prasad, R., & Liu, H. (2016). Prioritizing Adverse Drug Reaction and Drug Repositioning Candidates Generated by Literature-Based Discovery. 289–296. Rindflesch, T. C., & Fiszman, M. (2003). The interaction of domain knowledge and linguistic structure in natural language processing: Interpreting hypernymic propositions in biomedical text. Journal of Biomedical Informatics, 36(6), 462–477. Rindflesch, T. C., Kilicoglu, H., Fiszman, M., Rosemblat, G., & Shin, D. (2011). Semantic MEDLINE: An advanced information management application for biomedicine. Information Services and Use, 31(1–2), 15–21. Shim, J. S., & Liu, J. O. (2014). Recent advances in drug repositioning for the discovery of new anticancer drugs. International Journal of Biological Sciences, 10(7), 654–663. Swanson, D. R. (1986a). Fish oil, Raynaud’s syndrome, and undiscovered public knowledge. Perspectives in Biology and Medicine, 30(1), 7–18. Swanson, D. R. (1986b). Undiscovered Public Knowledge. The Library Quarterly, 56(2), 103–118. Swanson, D. R. (1988). Migraine and Magnesium: Eleven Neglected Connections. Perspectives in Biology and Medicine, 31(4), 526–557. Weng, J.-H. (2018). Literature-based Discovery for Drug Repurposing : An Improved Path-importance-based Approach by Considering Predicate Effects. (July). Yetisgen-Yildiz, M., & Pratt, W. (2009). A new evaluation methodology for literature-based discovery systems. Journal of Biomedical Informatics, 42(4), 633–643. Zhao, S. (2004). Named entity recognition in biomedical texts using an HMM model. (Grefenstette 1994), 84. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/73884 | - |
dc.description.abstract | 開發新藥物成本極高且風險極大,因此研究人員開始尋找許多替代方案。新藥上市有了新選擇——「舊藥新用」:從已經被核准做為臨床使用的藥物中,發現新的適應症用途。因舊藥已有許多完整的臨床藥物資訊及人體使用安全性資料,因此比起研發新藥,不但能夠縮短開發時間也能降低研發成本及風險。
1986年,Swanson利用資訊檢索與資料探勘技術,透過醫療文獻中的關係建構出醫療關係網路,希望能透過此網路實現舊藥新用,之後許多學者沿用此方法。我們沿用前一篇方法 (Lee, 2017) 提出的路徑重要性分類模型,從含有語意的醫療關係所建構出的網路中,利用機器學習技術透過已知的醫療語意關係,將這些醫療元件及關係轉換成富含資訊的向量,藉此擷取出含有醫療含義的特徵值,判斷此路徑對於某焦點藥物與候選疾病是否重要,針對某焦點藥物,依照分類的結果計算分數,對其候選疾病進行排序。 實驗證明我們加入的醫療語意向量能顯著提升原本的路徑重要性分類模型,藉此更有效找出潛在藥物新適應症。 | zh_TW |
dc.description.abstract | Drug development is extremely costly and risky, so researchers are looking for alternative approaches. There’s a new approach: Drug Repurposing, using existing drugs approved by FDA to find new indications. Compared with the development of new drugs, existing drugs have more complete clinical drug information and human safety data. It not only can shorten development time but also reduce the risks on R&D.
In 1986, Swanson proposed an approach using information retrieval and text mining techniques to construct a biomedical network composed of links between biomedical concepts from biomedical literatures. Through analyzing this network, drug repurposing can be achieved. Many researchers have followed this approach. Previous studies (Lee, 2017) proposed a path-importance-based approach. We follow this approach. In our research, we use machine learning techniques to convert biomedical entities and relations into representative biomedical vectors, discriminate whether a path is important or not and decide the candidate diseases given a focal drug. The empirical results show that the representative biomedical vectors can significantly improve the path-importance-based classification, which in turn can support effective drug repurposing. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T08:12:46Z (GMT). No. of bitstreams: 1 ntu-108-R06725004-1.pdf: 4830753 bytes, checksum: d23d89e03dedf0f88deacc164eca76e0 (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | Table of Contents
口試委員會審定書 i 誌謝 ii 中文摘要 iii Abstract iv Table of Contents vi List of Figures viii List of Tables ix Chapter1 Introduction 1 1.1 Background 1 1.2 Research Motivation and Objectives 4 Chapter2 Literature review 6 2.1 ABC Path Model 6 2.2 Path-importance-based Model 11 Chapter 3 Method 19 3.1 Data Resource 20 3.2 Concept Selection 21 3.3 Relation Selection 22 3.4 Knowledge Graph Embedding for Global Features 24 3.5 Global Feature Extraction 26 3.6 Path Classification 34 Chapter 4 Evaluation of Path Importance Classification 38 4.1 Path Importance Classifier Training Data 38 4.2 Global Feature Training 40 4.3 Benchmark 41 4.4 Evaluation Results 42 4.5 Additional Experiments 46 Chapter 5 Conclusions 49 5.1 Contributions 49 5.2 Limitations and Future Research Directions 50 References 51 Appendix A: Existing Drug Repurposing Cases 56 Appendix B: Significance of Predicates 58 | |
dc.language.iso | en | |
dc.title | 基於生物醫學文獻建構藥物新適應症預測模型 | zh_TW |
dc.title | Literature-based Discovery for Drug Repositioning: A Predicate-Pattern-Importance Ranking Approach | en |
dc.type | Thesis | |
dc.date.schoolyear | 107-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 簡立峰,楊錦生 | |
dc.subject.keyword | 舊藥新用,語義關係,監督式學習,路徑重要性分類模型,知識圖譜, | zh_TW |
dc.subject.keyword | drug repurposing,semantic predication,supervised learning,path importance classification,knowledge graph, | en |
dc.relation.page | 59 | |
dc.identifier.doi | 10.6342/NTU201903652 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2019-08-15 | |
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 |
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