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Generating association graphs of non-cooccurring text objects using transitive methods

Published: 13 March 2005 Publication History

Abstract

In this paper we discuss text data mining (TDM) mainly in the context of the biomedical domain, where we extract associations from MEDLINE text articles and construct association graphs. We explore two techniques, the co-occurrence method and transitive method. We propose a novel transitive method of finding associations that does not rely on meta-data, and compare the results with another known transitive method that uses metadata in text, to find a link/relationship between objects of interest. Co-occurrence of these terms (objects) is not required in the transitive methods to find out that they are associated. The results show that our proposed new method is as accurate as the known method that uses meta-data. This, in turn, implies that relationships can be discovered even when meta-data is not available or incomplete. A case study of a transitive association between a pair of genes (BRCAI---STATI) is also carried out to illustrate the effective hypothesis generating ability of our method. Based on the results, we conclude that our method can be used effectively for association extraction and also for hypothesis generation, which can later be validated through biological experimental analysis.

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cover image ACM Conferences
SAC '05: Proceedings of the 2005 ACM symposium on Applied computing
March 2005
1814 pages
ISBN:1581139640
DOI:10.1145/1066677
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 13 March 2005

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Author Tags

  1. association rules
  2. bio-informatics
  3. data mining
  4. knowledge discovery
  5. metadata
  6. text mining

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SAC05: The 2005 ACM Symposium on Applied Computing
March 13 - 17, 2005
New Mexico, Santa Fe

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

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  • (2023)Host-mediated gene engineering and microbiome-based technology optimization for sustainable agriculture and environmentFunctional & Integrative Genomics10.1007/s10142-023-00982-923:1Online publication date: 8-Feb-2023
  • (2020)Emerging Priorities for Microbiome ResearchFrontiers in Microbiology10.3389/fmicb.2020.0013611Online publication date: 19-Feb-2020
  • (2013)Text Mining for Neuroscience: A Co-morbidity Case StudyKnowledge-Based Systems in Biomedicine and Computational Life Science10.1007/978-3-642-33015-5_5(117-136)Online publication date: 2013
  • (2009)Hypotheses Generation Pertaining to Ayurveda Using Automated Vocabulary Generation and Transitive Text MiningProceedings of the 2009 International Conference on Network-Based Information Systems10.1109/NBiS.2009.30(200-205)Online publication date: 19-Aug-2009
  • (2008)The Impact of Directionality in Predications on Text MiningProceedings of the Proceedings of the 41st Annual Hawaii International Conference on System Sciences10.1109/HICSS.2008.443Online publication date: 7-Jan-2008
  • (2006)A comparative study of cells in inflammation, EAE and MS using biomedical literature data miningJournal of Biomedical Science10.1007/s11373-006-9120-814:1(67-85)Online publication date: 3-Nov-2006

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