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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 黃火鍊 | |
| dc.contributor.author | Yu-Ching Fang | en |
| dc.contributor.author | 方昱慶 | zh_TW |
| dc.date.accessioned | 2021-06-16T16:03:18Z | - |
| dc.date.available | 2014-07-08 | |
| dc.date.copyright | 2013-07-08 | |
| dc.date.issued | 2013 | |
| dc.date.submitted | 2013-07-02 | |
| dc.identifier.citation | Agirbasli, M., Sumerkan, M. C., Eren, F., & Agirbasli, D. (2011). The s447x variant of lipoprotein lipase gene is inversely associated with severity of coronary artery disease. Heart Vessels, 26(4), 457-463.
Agrawal, S., Dimitrova, N., Nathan, P., Udayakumar, K., Lakshmi, S. S., Sriram, S., et al. (2008). T2d-db: An integrated platform to study the molecular basis of type 2 diabetes. BMC Genomics, 9, 320. Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., et al. (2000). Gene ontology: Tool for the unification of biology. The gene ontology consortium. Nat Genet, 25(1), 25-29. Baumgartner, W. A., Jr., Cohen, K. B., Fox, L. M., Acquaah-Mensah, G., & Hunter, L. (2007). Manual curation is not sufficient for annotation of genomic databases. Bioinformatics, 23(13), i41-48. Baumgartner, W. A., Jr., Lu, Z., Johnson, H. L., Caporaso, J. G., Paquette, J., Lindemann, A., et al. (2008). Concept recognition for extracting protein interaction relations from biomedical text. Genome Biol, 9 Suppl 2, S9. Becker, K. G., Barnes, K. C., Bright, T. J., & Wang, S. A. (2004). The genetic association database. Nat Genet, 36(5), 431-432. Beeferman, D., Berger, A., & Lafferty, J. (1999). Statistical models for text segmentation. Machine learning, 34(1-3), 177-210. Berger, A. L., Pietra, V. J. D., & Pietra, S. A. D. (1996). A maximum entropy approach to natural language processing. Computational linguistics, 22(1), 39-71. Bosworth, C., Sachs, M. C., Duprez, D., Hoofnagle, A. N., Ix, J. H., Jacobs, D. R., Jr., et al. (2013). Parathyroid hormone and arterial dysfunction in the multi-ethnic study of atherosclerosis. Clin Endocrinol (Oxf). Bundschus, M., Dejori, M., Stetter, M., Tresp, V., & Kriegel, H. P. (2008). Extraction of semantic biomedical relations from text using conditional random fields. BMC Bioinformatics, 9, 207. Chen, S. F., & Rosenfeld, R. (1999). A gaussian prior for smoothing maximum entropy models:DTIC Document. Chen, T., Chang, L., Ma, J., Zhang, W., & Gao, F. (2009). Hoct: A highly scalable algorithm for training linear crf on modern hardware. Paper presented at the Data Mining Workshops, 2009. ICDMW'09. IEEE International Conference on. Cheng, D., Knox, C., Young, N., Stothard, P., Damaraju, S., & Wishart, D. S. (2008). Polysearch: A web-based text mining system for extracting relationships between human diseases, genes, mutations, drugs and metabolites. Nucleic Acids Res, 36(Web Server issue), W399-405. Chun, H. W., Tsuruoka, Y., Kim, J. D., Shiba, R., Nagata, N., Hishiki, T., et al. (2006). Extraction of gene-disease relations from medline using domain dictionaries and machine learning. Pac Symp Biocomput, 4-15. Cohen, K. B., & Hunter, L. (2008). Getting started in text mining. PLoS Comput Biol, 4(1), e20. Collier, N., Nobata, C., & Tsujii, J.-i. (2000). Extracting the names of genes and gene products with a hidden markov model. Paper presented at the Proceedings of the 18th conference on Computational linguistics-Volume 1. Cooper, D. N., Ball, E. V., & Mort, M. (2010). Chromosomal distribution of disease genes in the human genome. Genetic Testing and Molecular Biomarkers, 14(4), 441-446. Dabek, J., Swiderski, R., Glogowska-Ligus, J., & Pysz, P. (2012). Transcriptional activity of tumour necrosis factor alpha (tnf-alpha) in patients with subclinical coronary atherosclerosis - preliminary results. Folia Biol (Praha), 58(5), 209-214. Dai, H. J., Lai, P. T., & Tsai, R. T. (2010). Multistage gene normalization and svm-based ranking for protein interactor extraction in full-text articles. IEEE/ACM Trans Comput Biol Bioinform, 7(3), 412-420. Dai, H. J., Wu, J. C., Tsai, R. T., Pan, W. H., & Hsu, W. L. (2013). T-hod: A literature-based candidate gene database for hypertension, obesity and diabetes. Database (Oxford), 2013, bas061. Darroch, J. N., & Ratcliff, D. (1972). Generalized iterative scaling for log-linear models. The annals of mathematical statistics, 43(5), 1470-1480. Della Pietra, S., Della Pietra, V., & Lafferty, J. (1997). Inducing features of random fields. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 19(4), 380-393. Dingare, S., Nissim, M., Finkel, J., Manning, C., & Grover, C. (2005). A system for identifying named entities in biomedical text: How results from two evaluations reflect on both the system and the evaluations. Comparative and Functional Genomics, 6(1?), 77-85. Doms, A., & Schroeder, M. (2005). Gopubmed: Exploring pubmed with the gene ontology. Nucleic Acids Res, 33(Web Server issue), W783-786. Eckhouse, S. R., Jones, J. A., & Spinale, F. G. (2013). Gene targeting in ischemic heart disease and failure: Translational and clinical studies. Biochem Pharmacol, 85(1), 1-11. Eibel, B., Rodrigues, C. G., Giusti, II, Nesralla, I. A., Prates, P. R., Sant'Anna, R. T., et al. (2011). Gene therapy for ischemic heart disease: Review of clinical trials. Rev Bras Cir Cardiovasc, 26(4), 635-646. Erhardt, R. A., Schneider, R., & Blaschke, C. (2006). Status of text-mining techniques applied to biomedical text. Drug Discov Today, 11(7-8), 315-325. Falcao-Pires, I., & Leite-Moreira, A. F. (2012). Diabetic cardiomyopathy: Understanding the molecular and cellular basis to progress in diagnosis and treatment. Heart Fail Rev, 17(3), 325-344. Fang, Y. C., Huang, H. C., Chen, H. H., & Juan, H. F. (2008). Tcmgenedit: A database for associated traditional chinese medicine, gene and disease information using text mining. BMC Complement Altern Med, 8, 58. Fang, Y. C., Huang, H. C., & Juan, H. F. (2008). Meinfotext: Associated gene methylation and cancer information from text mining. BMC Bioinformatics, 9, 22. Fang, Y. C., Lai, P. T., Dai, H. J., & Hsu, W. L. (2011). Meinfotext 2.0: Gene methylation and cancer relation extraction from biomedical literature. BMC Bioinformatics, 12, 471. Formiga, F. R., Tamayo, E., Simon-Yarza, T., Pelacho, B., Prosper, F., & Blanco-Prieto, M. J. (2012). Angiogenic therapy for cardiac repair based on protein delivery systems. Heart Fail Rev, 17(3), 449-473. Fragos, K., Maistros, Y., & Skourlas, C. (2005). A weighted maximum entropy language model for text classification. Paper presented at the Proceeding of the 2nd International workshop on NLUCS, INSTICC Press. Fundel, K., Kuffner, R., & Zimmer, R. (2007). Relex--relation extraction using dependency parse trees. Bioinformatics, 23(3), 365-371. Goh, K. I., Cusick, M. E., Valle, D., Childs, B., Vidal, M., & Barabasi, A. L. (2007). The human disease network. Proc Natl Acad Sci U S A, 104(21), 8685-8690. Gorr, S. U., Wennblom, T. J., Horvath, S., Wong, D. T., & Michie, S. A. (2012). Text-mining applied to autoimmune disease research: The sjogren's syndrome knowledge base. BMC Musculoskelet Disord, 13, 119. Hahn, U., Wermter, J., Blasczyk, R., & Horn, P. A. (2007). Text mining: Powering the database revolution. Nature, 448(7150), 130. Hearst, M. A. (1999). Untangling text data mining. Paper presented at the Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics. Hirschman, L., Burns, G. A., Krallinger, M., Arighi, C., Cohen, K. B., Valencia, A., et al. (2012). Text mining for the biocuration workflow. Database (Oxford), 2012, bas020. Hoffmann, R., & Valencia, A. (2005). Implementing the ihop concept for navigation of biomedical literature. Bioinformatics, 21 Suppl 2, ii252-258. Hou, W. J., & Chen, H. Y. (2013). Rule extraction in gene-disease relationship discovery. Gene, 518(1), 132-138. Hristovski, D., Peterlin, B., Mitchell, J. A., & Humphrey, S. M. (2005). Using literature-based discovery to identify disease candidate genes. Int J Med Inform, 74(2-4), 289-298. Hu, Y., Hines, L. M., Weng, H., Zuo, D., Rivera, M., Richardson, A., et al. (2003). Analysis of genomic and proteomic data using advanced literature mining. J Proteome Res, 2(4), 405-412. Hulbert, E. M., Smink, L. J., Adlem, E. C., Allen, J. E., Burdick, D. B., Burren, O. S., et al. (2007). T1dbase: Integration and presentation of complex data for type 1 diabetes research. Nucleic Acids Res, 35(Database issue), D742-746. Jensen, L. J., Saric, J., & Bork, P. (2006). Literature mining for the biologist: From information retrieval to biological discovery. Nat Rev Genet, 7(2), 119-129. Jiang, T. T., Chen, X., Li, T. T., Zhang, F. G., Xie, Y., Zhang, J. N., et al. (2012). [genetic scanning on chromosome 8 loci for coronary heart disease]. Yi Chuan, 34(8), 1043-1049. Jimeno, A., Jimenez-Ruiz, E., Lee, V., Gaudan, S., Berlanga, R., & Rebholz-Schuhmann, D. (2008). Assessment of disease named entity recognition on a corpus of annotated sentences. BMC Bioinformatics, 9 Suppl 3, S3. Kanehisa, M., Goto, S., Sato, Y., Furumichi, M., & Tanabe, M. (2012). Kegg for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res, 40(Database issue), D109-114. Keshava Prasad, T. S., Goel, R., Kandasamy, K., Keerthikumar, S., Kumar, S., Mathivanan, S., et al. (2009). Human protein reference database--2009 update. Nucleic Acids Res, 37(Database issue), D767-772. Klinger, R., Friedrich, C. M., Fluck, J., & Hofmann-Apitius, M. (2007). Named entity recognition with combinations of conditional random fields. Paper presented at the Proc. of the Second BioCreative Challenge Evaluation Workshop. Klinger, R., & Tomanek, K. (2007). Classical probabilistic models and conditional random fields:TU, Algorithm Engineering. Krallinger, M., Leitner, F., & Valencia, A. (2010). Analysis of biological processes and diseases using text mining approaches. Methods Mol Biol, 593, 341-382. Kusama, Y., Kodani, E., Nakagomi, A., Otsuka, T., Atarashi, H., Kishida, H., et al. (2011). Variant angina and coronary artery spasm: The clinical spectrum, pathophysiology, and management. J Nippon Med Sch, 78(1), 4-12. Lafferty, J., McCallum, A., & Pereira, F. C. N. (2001). Conditional random fields: Probabilistic models for segmenting and labeling sequence data. Lau, W. W., Johnson, C. A., & Becker, K. G. (2007). Rule-based human gene normalization in biomedical text with confidence estimation. Paper presented at the Comput Syst Bioinformatics Conf. Le, Z. (2004). Maximum entropy modeling toolkit for python and c++. Natural Language Processing Lab, Northeastern University, China. Leser, U., & Hakenberg, J. (2005). What makes a gene name? Named entity recognition in the biomedical literature. Brief Bioinform, 6(4), 357-369. Liu, H., Liu, W., Liao, Y., Cheng, L., Liu, Q., Ren, X., et al. (2011). Cadgene: A comprehensive database for coronary artery disease genes. Nucleic Acids Res, 39(Database issue), D991-996. Liu, Y., Shriberg, E., Stolcke, A., & Harper, M. (2005). Comparing hmm, maximum entropy, and conditional random fields for disfluency detection. Paper presented at the Proc. of InterSpeech, Lisbon. Majoros, W. H., Subramanian, G. M., & Yandell, M. D. (2003). Identification of key concepts in biomedical literature using a modified markov heuristic. Bioinformatics, 19(3), 402-407. Malouf, R. (2002). A comparison of algorithms for maximum entropy parameter estimation. Paper presented at the Proceedings of the sixth conference on natural language learning (CoNLL-2002). Mingliang, R., Bo, Z., & Zhengguo, W. (2011). Stem cells for cardiac repair: Status, mechanisms, and new strategies. Stem Cells Int, 2011, 310928. Muller, H. M., Rangarajan, A., Teal, T. K., & Sternberg, P. W. (2008). Textpresso for neuroscience: Searching the full text of thousands of neuroscience research papers. Neuroinformatics, 6(3), 195-204. Musio, A., Mariani, T., Vezzoni, P., & Frattini, A. (2002). Heterogeneous gene distribution reflects human genome complexity as detected at the cytogenetic level. Cancer genetics and cytogenetics, 134(2), 168-171. Narayanaswamy, M., Ravikumar, K. E., & Vijay-Shanker, K. (2005). Beyond the clause: Extraction of phosphorylation information from medline abstracts. Bioinformatics, 21 Suppl 1, i319-327. Nigam, K., Lafferty, J., & McCallum, A. (1999). Using maximum entropy for text classification. Paper presented at the IJCAI-99 workshop on machine learning for information filtering. Ramani, A. K., Bunescu, R. C., Mooney, R. J., & Marcotte, E. M. (2005). Consolidating the set of known human protein-protein interactions in preparation for large-scale mapping of the human interactome. Genome Biol, 6(5), R40. Ratnaparkhi, A. (1996). A maximum entropy model for part-of-speech tagging. Paper presented at the Proceedings of the conference on empirical methods in natural language processing. Ratnaparkhi, A. (1997). A simple introduction to maximum entropy models for natural language processing. IRCS Technical Reports Series, 81. Ratnaparkhi, A. (1998). Maximum entropy models for natural language ambiguity resolution. University of Pennsylvania. Rebholz-Schuhmann, D., Kirsch, H., Arregui, M., Gaudan, S., Riethoven, M., & Stoehr, P. (2007). Ebimed--text crunching to gather facts for proteins from medline. Bioinformatics, 23(2), e237-244. Rebholz-Schuhmann, D., Oellrich, A., & Hoehndorf, R. (2012). Text-mining solutions for biomedical research: Enabling integrative biology. Nat Rev Genet, 13(12), 829-839. Rzhetsky, A., Iossifov, I., Koike, T., Krauthammer, M., Kra, P., Morris, M., et al. (2004). Geneways: A system for extracting, analyzing, visualizing, and integrating molecular pathway data. J Biomed Inform, 37(1), 43-53. Saric, J., Jensen, L. J., Ouzounova, R., Rojas, I., & Bork, P. (2006). Extraction of regulatory gene/protein networks from medline. Bioinformatics, 22(6), 645-650. Schuler, G. D., Epstein, J. A., Ohkawa, H., & Kans, J. A. (1996). Entrez: Molecular biology database and retrieval system. Methods Enzymol, 266, 141-162. Shah, P. K., Jensen, L. J., Boue, S., & Bork, P. (2005). Extraction of transcript diversity from scientific literature. PLoS Comput Biol, 1(1), e10. Smith, T. F., & Waterman, M. S. (1981). Identification of common molecular subsequences. J Mol Biol, 147(1), 195-197. Song, Y., Kim, E., Lee, G. G., & Yi, B.-K. (2005). Posbiotm-ner: A trainable biomedical named-entity recognition system. Bioinformatics, 21(11), 2794-2796. Sung, C.-L., Lee, C.-W., Yen, H.-C., & Hsu, W.-L. (2009). Alignment-based surface patterns for factoid question answering systems. Integrated Computer-Aided Engineering, 16(3), 259-269. Temkin, J. M., & Gilder, M. R. (2003). Extraction of protein interaction information from unstructured text using a context-free grammar. Bioinformatics, 19(16), 2046-2053. Tiffin, N., Kelso, J. F., Powell, A. R., Pan, H., Bajic, V. B., & Hide, W. A. (2005). Integration of text- and data-mining using ontologies successfully selects disease gene candidates. Nucleic Acids Res, 33(5), 1544-1552. Tsai, R. T., Lai, P. T., Dai, H. J., Huang, C. H., Bow, Y. Y., Chang, Y. C., et al. (2009). Hypertengene: Extracting key hypertension genes from biomedical literature with position and automatically-generated template features. BMC Bioinformatics, 10 Suppl 15, S9. Tsai, R. T., Sung, C. L., Dai, H. J., Hung, H. C., Sung, T. Y., & Hsu, W. L. (2006). Nerbio: Using selected word conjunctions, term normalization, and global patterns to improve biomedical named entity recognition. BMC Bioinformatics, 7 Suppl 5, S11. van Ballegooijen, A. J., Visser, M., Cotch, M. F., Arai, A. E., Garcia, M., Harris, T. B., et al. (2013). Serum vitamin d and parathyroid hormone in relation to cardiac structure and function: The iceland-mi substudy of ages-reykjavik. J Clin Endocrinol Metab. von Mering, C., Jensen, L. J., Snel, B., Hooper, S. D., Krupp, M., Foglierini, M., et al. (2005). String: Known and predicted protein-protein associations, integrated and transferred across organisms. Nucleic Acids Res, 33(Database issue), D433-437. Won, Y. W., Lee, M., Kim, H. A., Bull, D. A., & Kim, S. W. (2013). Hypoxia-inducible plasmid expressing both mishp-1 and ho-1 for the treatment of ischemic disease. J Control Release, 165(1), 22-28. Wren, J. D., & Garner, H. R. (2004). Shared relationship analysis: Ranking set cohesion and commonalities within a literature-derived relationship network. Bioinformatics, 20(2), 191-198. Yang, Q., Chen, Y., & Yong, W. (2013). Foxp3 genetic variant and risk of acute coronary syndrome in chinese han population. Cell Biochem Funct. Yoshida, T., Kato, K., Oguri, M., Horibe, H., Kawamiya, T., Yokoi, K., et al. (2011). Association of polymorphisms of btn2a1 and ilf3 with myocardial infarction in japanese individuals with different lipid profiles. Mol Med Rep, 4(3), 511-518. Yoshioka, J., Chutkow, W. A., Lee, S., Kim, J. B., Yan, J., Tian, R., et al. (2012). Deletion of thioredoxin-interacting protein in mice impairs mitochondrial function but protects the myocardium from ischemia-reperfusion injury. J Clin Invest, 122(1), 267-279. Yue, P., Melamud, E., & Moult, J. (2006). Snps3d: Candidate gene and snp selection for association studies. BMC Bioinformatics, 7, 166. Zhou, G., Zhang, J., Su, J., Shen, D., & Tan, C. (2004). Recognizing names in biomedical texts: A machine learning approach. Bioinformatics, 20(7), 1178-1190. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/62493 | - |
| dc.description.abstract | 隨著科學文獻近二十年大量發表，如何系統化地處理與分析其中的生物醫學名詞成為一門重要的領域。本論文將專注於基因與特定疾病的關聯性擷取以及在資料庫系統上的應用。
缺血性心臟病 (Ischaemic Heart Disease, IHD) 已被世界衛生組織列為全球十大死因之首，尤其好發在中、高收入的國家。最常造成IHD發生的原因是冠狀動脈硬化，其它風險因子則有高血脂及基因多樣化等等。傳統的療法已證實有諸多問題，特別是針對有代謝疾病的人效果有限，因此許多研究已開始尋找其他的治療方式，目前已有多篇文獻指出基因療法可能是一個新的方向，然而透過文件探勘來廣泛地找出IHD致病基因及開發相關資料庫的研究仍是相當缺乏。 因此我們將IHD選為研究對象，透過自動化文件探勘技術來分析上萬篇的生物醫學文獻，進而擷取基因與IHD的關聯，主要的探勘過程包括基因名詞辨識、基因名詞正規化、疾病名詞辨識、語料庫標註與模型訓練，並將分析結果建構成一個資料庫系統，同時也把其他相關的資訊，例如蛋白質交互作用、單一核苷酸變異 (SNP) 等與本系統整合起來。我們的關聯性擷取模型經過評量後顯示另人滿意的表現，其精準率 (F-score) 達到80.81%，此外，與其他以人工方式所建立之資料庫比較後也指出，以自動化機器學習為基礎的文件探勘不但在準確度上有一定水準，且所找出的可能致病基因數量也遠大於前者，目前約有700筆致病基因記錄於資料庫中。再者，文件探勘的結果與各種整合資訊，可協助研究人員進一步了解及推論其他可能的致病基因及相關機制。 這是第一個以文件探勘的方式來分析基因與IHD的研究，並透過資料庫系統來呈現其關聯性，未來希望能以此方法為基礎加上專門領域知識來分析更多的特定疾病，且隨著相關應用工具的改良也能更致力改善本系統在各方面的表現。 | zh_TW |
| dc.description.abstract | As the scientific literatures grow exponentially over the past 20 years, how to systematically process and analyze the biomedical named entities becomes an important field. In this thesis, we focus on relation extraction for genes and specific diseases and its application to database system.
Ischaemic heart disease (IHD), the most common type of heart disease, has been indicated the top one of the world 10 leading causes of death by World Health Organization, especially in middle and high-income countries. Coronary arteriosclerosis is the most frequent cause of IHD and other risk factors include hyperlipidemia, gene polymorphisms etc. Classic therapeutics for IHD is less effective in individuals with the metabolic syndrome. Several studies have shown that gene therapy may provide a novel means for IHD treatment. However, there is little research about discovering comprehensive gene and IHD relations from text mining and developing related databases. Therefore, we exploit the contemporary text mining technologies to find genes related to IHD from the literature and develop a database system that provides convenient and accurate access of the extracted information and integrated data such as protein-protein interactions and single-nucleotide polymorphisms. The testing results indicate the relation extraction model achieves a satisfactory F-score of 80.81% for IHD. In addition, comparisons with other related databases manually constructed show our text mining approach based on automatic machine learning could not only perform precisely but also dig out much more disease candidate genes. At present, there are about 700 candidate genes available in our database for IHD. Furthermore, the text mining results and the website integrated information may help researchers to understand and infer putative disease genes and mechanisms contributing to the disease. This is the first study focusing on gene and IHD relation extraction by automatic literature text mining and developing a database system for result presentation. In the future, we would like to analyze other specific diseases using corresponding domain knowledge and the similar approach. We also want to adopt better tools to enhance the system performance. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T16:03:18Z (GMT). No. of bitstreams: 1 ntu-102-D94b43002-1.pdf: 3128481 bytes, checksum: 06663ccc25a6bc4b22a07d1a38bce070 (MD5) Previous issue date: 2013 | en |
| dc.description.tableofcontents | 摘要 i
Abstract ii Publications iv Table of Contents v List of Figures vi List of Tables vii Chapter 1 Introduction 1 Chapter 2 Related Work 8 Chapter 3 Biomedical Named Entity Relation Extraction 12 3.1 Data Collection and Pre-processing 12 3.2 Methods 14 3.2.1 Gene Named Entity Recognition 14 3.2.2 Disease Named Entity Recognition 17 3.2.3 Maximum Entropy Models 18 3.3 Results and Discussion 25 3.3.1 Evaluation 25 3.3.2 Comparison 25 Chapter 4 Database Development 28 4.1 Website Interface and Implementation 28 4.1.1 TGI Search Page 32 4.1.2 TGI Search Results by Genes 33 4.1.3 TGI Search Results by Chromosome 37 4.1.4 TGI Search Results by Literature 38 4.1.5 TGI Search Results by PPIs and Pathways 40 4.1.6 TGI Search Results by Gene Ontology 41 4.1.7 Navigation 42 4.2 Results and Discussion 44 4.2.1 System Comparison 44 4.2.2 Relation Extraction Performance 47 4.2.3 Chromosome Distribution 48 4.2.4 Disease Pathway and Term Distribution 50 4.2.5 Functional Classification 52 4.2.6 Novel Disease Candidate Gene Inference 53 Chapter 5 Conclusion and Future Work 58 References 59 | |
| dc.language.iso | en | |
| dc.subject | 文件探勘 | zh_TW |
| dc.subject | 關聯性擷取 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 資料庫系統 | zh_TW |
| dc.subject | 致病基因 | zh_TW |
| dc.subject | 資訊整合 | zh_TW |
| dc.subject | Database System | en |
| dc.subject | Text Mining | en |
| dc.subject | Relation Extraction | en |
| dc.subject | Disease Candidate Gene | en |
| dc.subject | Information Integration | en |
| dc.subject | Machine Learning | en |
| dc.title | 生物醫學名詞關聯擷取及資料庫系統之應用 | zh_TW |
| dc.title | Biomedical Named Entity Relation Extraction and Its Application to Database System | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 101-2 | |
| dc.description.degree | 博士 | |
| dc.contributor.coadvisor | 許聞廉 | |
| dc.contributor.oralexamcommittee | 蔡宗翰,宋定懿,陳倩瑜 | |
| dc.subject.keyword | 文件探勘,關聯性擷取,機器學習,資料庫系統,致病基因,資訊整合, | zh_TW |
| dc.subject.keyword | Text Mining,Relation Extraction,Machine Learning,Database System,Disease Candidate Gene,Information Integration, | en |
| dc.relation.page | 65 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2013-07-02 | |
| dc.contributor.author-college | 生命科學院 | zh_TW |
| dc.contributor.author-dept | 分子與細胞生物學研究所 | zh_TW |
| 顯示於系所單位： | 分子與細胞生物學研究所 | |
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