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A Tentative Study of Disjoint Literature Discovery Based on Transitive Closure ——Take Cancer Drug Target for Example |
Yang Yuan1,2,3, Gao Liubin2 |
1. National Science Library, Chinese Academy of Sciences, Beijing 100190,China;
2. Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203,China;
3. Graduate University of Chinese Academy of Sciences, Beijing 100049, China |
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Abstract Based on the principle of disjoint literature knowledge discovery,transitive closure in discrete mathematics is applied to find potential associations among drug targets,which confirms that transitive closure based disjoint literature knowledge discovery is achievable and effective. What’s more,the paper makes the original three-step model to multi-step knowledge discovery model,which can get more potential associations but ensure relative high precision and high recall at the same time.
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Received: 25 September 2010
Published: 07 January 2011
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[1] Swanson D R. Undiscovered Public Knowledge [J]. Library Quarterly, 1986, 56(2):103-118.
[2] Lindsay R K, Gordon M D. Literature Based Discovery by Lexical Statistics [J]. Journal of American Society for Information Science, 1999,50(7):674-685.
[3] Gordon M D, Dumais S. Using Latent Semantic Indexing for Literature Based Discovery [J]. Journal of American Society for Information Science, 1998, 4(8): 674-685.
[4] Weeber M, Klein H, de Jong-van den Berg L T W, et al. Using Concepts in Literature-based Discovery: Simulating Swanson’s Reynaud-fish Oil and Migraine-Magnesium Discoveries [J]. Journal of the American Society for Information Science and Technology, 2001, 52(7):548-557.
[5] Stegmann J,Grohmann G. Hypothesis Generation Guided by Co-word Clustering [J]. Scientometrics, 2003, 56(1): 111-135.
[6] Huang W, Nakamori1 Y, Wang S Y, et al. Mining Scientific Literature to Predict New Relationships [J]. Intelligent Data Analysis, 2005, 9(2):219-234.
[7] Yetisgen-Yildiz M,Pratt W. Using Statistical and Knowledge-based Approaches for Literature Based Discovery [J]. Journal of Biomedical Informatics,2006,39(6):600-611.
[8] Wren J D. Extending the Mutual Information Measure to Rank Inferred Literature Relationship [J]. BMC Bioinformatics, 2004, 5(1):145-158.
[9] Hristovski D, Dzeroski S, Peterlin B, et al. Supporting Discovery in Medicine by Association Rule Mining of Bibliographic Database. In:Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery.2000:446-451.
[10] 张云秋,冷伏海.基于文献内聚度的非相关文献知识发现排序方法研究 [J]. 现代图书情报技术,2009(6):50-54.
[11] 张云秋,于双成.基于MeSH加权的非相关文献知识发现排序方法研究 [J]. 情报理论与实践,2009,32(7):113-115.
[12] 张云秋,郭科磊. 基于双向词频统计的非相关文献知识发现排序方法研究 [J]. 情报科学,2009,27(8):1240-1244.
[13] 张云秋,冷伏海.非相关文献知识发现初始集过滤方法的试验研究 [J]. 图书情报工作,2009,53(16):116-119.
[14] 陈显强.二元关系的传递性和传递闭包探讨 [J]. 数学的实践与认识,2004,34(9):135-137.
[15] Thomoson Pharma介绍.百度文库.http://wenku.baidu.com/view/b5498d7931b765ce0508145d.html.
[16] Medline介绍.百度百科.http://baike.baidu.com/view/686028.htm.
[17] Salton G, Wong A, Yang C S. A Vector Space Model for Automatic Indexing [J].Communications of the ACM,1975,18(11):613-620.
[18] Stephens M, Palakal M, Mukhopadhyay S, et al. Detecting Gene Relations from Medline Abstracts [J]. Pacific Symposium on Biocomputing, 2001(6):483-496.
[19] Warshall S. A Theorem on Boolean Matrics [J]. Journal of ACM, 1962, 9(1): 11-12.
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