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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 魏志平 | |
dc.contributor.author | ZIH-SIAN YANG | en |
dc.contributor.author | 楊子嫻 | zh_TW |
dc.date.accessioned | 2021-07-11T14:42:19Z | - |
dc.date.available | 2021-11-02 | |
dc.date.copyright | 2016-11-02 | |
dc.date.issued | 2016 | |
dc.date.submitted | 2016-08-18 | |
dc.identifier.citation | Adams, C. P., & Brantner, V. V. (2006). Estimating the cost of new drug development: is it really $802 million? Health Affairs, 25(2), pp. 420-428.
Baker, N. C., & Hemminger, B. M. (2010). Mining connections between chemicals, proteins, and diseases extracted from Medline annotations. Journal of Biomedical Informatics, 43(4), pp. 510-519. 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 (pp. 129-136). ACM. Chang, Y. T. (2015). Computational drug repositioning: a learning to rank approach with multiple data sources. Unpublished Master Thesis, Department of Information Management, National Taiwan University, Taiwan. Chen, B., Ding, Y., & Wild, D. J. (2012). Assessing drug target association using semantic linked data. PLOS Computational Biology, 8(7), p. e1002574. Chen, K. A. (2013). Mining biomedical literature and ontologies for drug repositioning discovery. Unpublished Master Thesis, Department of Information Management, National Taiwan University, Taiwan. Cheng, F., Liu, C., Jiang, J., Lu, W., Li, W., Liu, G., . . . Tang, Y. (2012). Prediction of drug-target interactions and drug repositioning via network-based inference. PLOS Computational Biology, 8(5), p. e1002503. Chiang, A., & Butte, A. (2009). Systematic evaluation of drug–disease relationships to identify leads for novel drug uses. Clinical Pharmacology & Therapeutics, 86(5), pp. 507-510. Davis, A. P., Murphy, C. G., Johnson, R., Lay, J. M., Lennon-Hopkins, K., Saraceni-Richards, C., . . . Mattingly, C. J. (2015). The comparative toxicogenomics database's 10th year anniversary: update 2015. Nucleic Acids Research, 43(Database issue), pp. D914-20. 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), p. e60618. Ghofrani, H. A., Osterloh, I. H., & Grimminger, F. (2006). Sildenafil: from angina to erectile dysfunction to pulmonary hypertension and beyond. Nature Reviews Drug Discovery, 5(8), pp. 689-702. 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), p. 496. Grabowski, H. (2004). Are the economics of pharmaceutical research and development changing? PharmacoEconomics, 22(2), pp. 15-24. Hamosh, A., Scott, A. F., Amberger, J. S., Bocchini, C. A., & McKusick, V. A. (2005). Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Research, 33(suppl 1), pp. D514-D517. Hristovski, D., Borut Peterlin, J. A., & Humphrey, S. M. (2005). Using literature-based discovery to identify disease candidate genes. International Journal of Medical Informatics, 74(2), pp. 289-298. Hristovski, D., Friedman, C., Rindflesch, T. C., & Peterlin, B. (2006). Exploiting semantic relations for literature-based discovery. AMIA Annual Symposium Proceedings (p. 349). American Medical Informatics Association. Katz, L. (1953). A new status index derived from sociometric analysis. Psychometrika, 18(1), pp. 39-43. Kilicoglu, H., Shin, D., Fiszman, M., Rosemblat, G., & Rindflesch, T. C. (2012). SemMedDB: a PubMed-scale repository of biomedical semantic predications. Bioinformatics, 28(23), pp. 3158-3160. Kissa, M., Tsatsaronis, G., & Schroeder, M. (2015). Prediction of drug gene associations via ontological profile similarity with application to drug repositioning. Methods, 74, pp. 71-82. Lenz, W. (1988). A short history of thalidomide embryopathy. Teratology, 38(3), pp. 203-215. 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), p. e1000450. Navlakha, S., & Kingsford, C. (2010). The power of protein interaction networks for associating genes with diseases. Bioinformatics, 26(8), pp. 1057-1063. 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), pp. i277-i285. 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), pp. 462-477. Smalheiser, N. R., & Swanson, D. R. (1998). Using ARROWSMITH: a computer-assisted approach to formulating and assessing scientific hypotheses. Computer Methods and Programs in Biomedicine, 57(3), pp. 149-153. Song, M., Heo, G. E., & Ding, Y. (2015). SemPathFinder: semantic path analysis for discoveringpublicly unknown knowledge. Journal of Informetrics, 9(4), pp. 686-703. Swanson, D. R. (1986). Undiscovered public knowledge. The Library Quarterly, pp. 103-118. 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), pp. 548-557. Wishart, D. S., Knox, C., Guo, A. C., Shrivastava, S., Hassanali, M., Stothard, P., . . . Woolsey, J. (2006). DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Research, 34(suppl 1), pp. D668-D672. Wren, J. D. (2004). Extending the mutual information measure to rank inferred literature relationship. BMC Bioinformatics, 5(1), p. 145. 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), pp. 389-398. Wu, X., Jiang, R., Zhang, M. Q., & Li, S. (2008). Network-based global inference of human disease genes. Molecular Systems Biology, 4(1), p. 189. Xu, R., & Wang, Q. (2013). Large-scale extraction of accurate drug-disease treatment pairs from biomedical literature for drug repurposing. BMC Bioinformatics, 14(1), p. 181. Yetisgen-Yildiz, M., & Pratt, W. (2009). A new evaluation methodology for literature-based discovery systems. Journal of Biomedical Informatics, 42(4), pp. 633-643. Zhang, R., Cairelli, M. J., Fiszman, M., Kilicoglu, H., Rindflesch, T. C., Pakhomov, S. V., & Melton, G. B. (2014). Exploiting literature-derived knowledge and semantics to identify potential prostate cancer drugs. Cancer Informatics, 13(Suppl 1), pp. 103-111. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/78103 | - |
dc.description.abstract | 一般而言,藥物開發的過程耗時而且成本高昂,但失敗的機率卻非常高。「舊藥新用」提供了藥物開發的另一個可行方向。它利用既有藥物的作用機轉或與其他生物因子間(例如:基因、蛋白質…)的關係來找出可能的新適應症。而那些未能通過藥物開發流程的藥物也是「舊藥新用」方法實施的對象之一。雖然目前已經有許多藥商、醫藥研究人員和學者引入這個方法,但仍有許多改善的空間。因此,本研究提出SemDRLTR模型,其利用自然語言技術處理後的醫學文獻資料,加上多個醫藥知識庫來建立關係網路,並以Swanson ABC模型和Chen Path 模型中不同權重計算方法與排序方法的組合作為特徵值(Feature),利用這些特徵值來進行監督式排序學習(Supervised Learning to Rank),再以學習後的模型來預測藥物可能的新適應症。另外,本研究亦提出一個整合In-predication和Co-occurrence概念的方法以期能提高SemDRLTR模型的準確度。從實驗結果可知,SemDRLTR模型能夠比既有的方法表現更好,其中In-predication的表現較Co-occurrence佳,而整合式的方法能夠達到更好的效能提升,這代表將此整合式方法運用至我們的SemDRLTR模型中能夠更有效地找出潛在的藥物與疾病關係。 | zh_TW |
dc.description.abstract | The process of drug development consumes much time and money but it often fails during clinical tests or other stages of drug development. Drug repositioning gives another way for drug discovery. Its goal is to find new indications for existing drugs or those drugs which ever failed at one of drug development stages. Although many researchers engage in this topic, there is still room for improvement. Hence, we propose a Semantic-based Drug Repositioning Learning-to-Rank (SemDRLTR) method, which considers the semantic relation between concepts. It is based on Swanson’s ABC model as well as Chen’s Path model and combines literature and ontologies to construct a comprehensive concept network. We further propose hybrid methods that combine the in-predication method and the co-occurrence method. From the results of our experiments, it is proven that in-predication, which keeps the meaning of relation between concepts, outperforms the co-occurrence method, which only considers whether concepts co-occur or not and the hybrid methods further perform better than either one. | en |
dc.description.provenance | Made available in DSpace on 2021-07-11T14:42:19Z (GMT). No. of bitstreams: 1 ntu-105-R03725020-1.pdf: 1978668 bytes, checksum: 1d2ea2d74352833f25ca8af6930167b4 (MD5) Previous issue date: 2016 | en |
dc.description.tableofcontents | 誌謝 i
中文摘要 ii Abstract iii Table of Contents iv 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 7 2. 1 Literature-based Discovery 7 2. 2 Ontology-based Discovery 11 2. 3 Integration of Literature and Ontology 13 Chapter 3 Design of Our SemDRLTR Method 18 3. 1 Literature-based Concept Network Construction 20 3. 1. 1 Concept and Relation Extraction 20 3. 1. 2 MeSH Annotations Mapping and Filtering 21 3. 1. 3 In-Predication and Co-occurrence Methods 22 3. 2 Ontology-based Concept Network Construction 24 3. 2. 1 Ontology Introduction 24 3. 2. 2 MeSH Annotations Mapping and Filtering 25 3. 3 Feature Extraction 26 3. 3. 1 Link Weighting Measures 26 3. 3. 2 Target Term Ranking Algorithms 28 3. 3. 3 Global Measures 31 3. 3. 4 Summary of Our Features 34 3. 4 Ranking Model Learning 35 Chapter 4 Evaluation and Results 37 4. 1 Evaluation Design 37 4. 2 Tuning Experiments for Non-Learning Models 44 4. 3 Comparative Experiments 51 4. 4 In-depth Experiment – Global Measures 55 Chapter 5 Hybrid Methods 58 5. 1 Design of Hybrid Methods 58 5. 2 Evaluation and Results 59 Chapter 6 Conclusion and Future Work 63 References 66 Appendix 71 | |
dc.language.iso | en | |
dc.title | 以生物醫學文獻建構之語義網路預測藥物新適應症 | zh_TW |
dc.title | Literature-based Discovery for Drug Repositioning: A Semantic-based Concept Network Approach | en |
dc.type | Thesis | |
dc.date.schoolyear | 104-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 劉敦仁,盧信銘,蕭斐元 | |
dc.subject.keyword | 舊藥新用,醫學文獻探勘,醫藥知識庫,語義網路,排序學習, | zh_TW |
dc.subject.keyword | Drug repositioning,Literature-based discovery,Biomedical literature mining,Biomedical ontology,Semantic network,Learning to rank, | en |
dc.relation.page | 90 | |
dc.identifier.doi | 10.6342/NTU201603086 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2016-08-19 | |
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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