Abstract
We introduce knowledge discovery for vasculitic diseases in this paper. Vasculitic diseases affect some organs and tissues and diagnosing can be quite difficult. Biomedical literature can contain hidden and useful knowledge for biomedical research and we develop a study based on co-occurrence analysis by using the articles in MEDLINE which is a widely used database.The mostly seen vasculitic diseases are selected to explore hidden patterns. We select PolySearch system as a web based biomedical text mining tool to find organs and tissues in the articles and create two separate datasets with their frequencies for each disease. After forming these datasets, we apply hierarchical clustering analysis to find similarities between the diseases. Clustering analysis reveals some similarities between diseases. We think that the results of clustered diseases positively affect on the medical research of vasculitic diseases especially during the diagnosis and certain similarities can provide different views to medical specialists.
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References
Al-Mubaid, H., Singh, R.K.: A new text mining approach for finding protein-to-protein associations. American Journal of Biochemistry and Biotechnology 1(3), 145–152 (2005)
Solka, J.L.: Text Data Mining: Theory and Methods. Statistics Surveys 2, 94–112 (2008)
Vasculitis Foundation Canada, http://www.vasculitis.ca/
Vasculitis Foundation, http://www.vasculitisfoundation.org/node/1589
Uramoto, N., Matsuzawa, H., Nagano, T., Murakami, A., Takeuchi, H., Takeda, K.: A text-mining system for knowledge discovery from biomedical documents. IBM Systems Journal 43(3), 516–533 (2004)
Zhou, W., Smalheiser, N.R., Yu, C.: A tutorial on information retrieval: basic terms and concepts. Journal of Biomedical Discovery and Collaboration 1(2) (2006)
United States National Library of Medicine (NLM), http://www.nlm.nih.gov/databases/databases_medline.html
Cheng, D., Knox, C., Young, N., Stothard, P.: PolySearch: a web-based text mining system for extracting relationships between human diseases, genes, mutations, drugs and metabolites. Nucleic Acids Research 36, 399–405 (2008)
Perez-Iratxeta, C., Pérez, A.J., Bork, P., Andrade, M.A.: Update on XplorMed: a web server for exploring scientific literature. Nucleic Acids Research 31(13), 3866–3868 (2003)
Lin, S.M., McConnell, P., Johnson, K.F., Shoemaker, J.: MedlineR: an open source library in R for Medline literature data mining. Bioinformatics 18(20), 3659–3661 (2004)
Maier, H., Döhr, S., Grote, K., O’Keeffe, S.: LitMiner and WikiGene: identifying problem-related key players of gene regulation using publication abstracts. Nucleic Acids Research 33, 779–782 (2005)
Jelier, R., Schuemie, M.J., Veldhoven, A., Dorssers, L.C., Jenster, G., Kors, G.J.A.: Anni 2.0: a multipurpose text-mining tool for the life sciences. Genome Biology 9(6) (2008)
Tsuruoka, Y., Tsujii, J., Ananiadou, S.: FACTA: a text search engine for finding associated biomedical concepts. Bioinformatics Applications Note 24(21), 2559–2560 (2008)
Krallinger, M., Leither, F., Valencia, A.: Analysis of Biological Processes and Diseases Using Text Mining Approaches. Bioinformatics Methods in Clinical Research Series: Methods in Molecular Biology 593, 341–382 (2009)
Holland, S.M.: Cluster Analysis. Depatrment of Geology, University of Georgia, Athens, GA 30602-2501 (2006)
Beckstead, J.W.: Using Hierarchical Cluster Analysis in Nursing Research. Western Journal of Nursing Research 24(307), 307–319 (2002)
Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Addison Wesley, Reading (2006)
Open Source Clustering Software, overview, http://bonsai.ims.u-tokyo.ac.jp/~mdehoon/software/cluster/
Astikainen, K., Kaven, R.: Statistical Analysis of Array Data:-Dimensionality Reduction, Clustering. Research Seminar on Data Analysis for Bioinformatics
Sato, E.I., Coelho Andrade, L.E.: Systemic vasculitis: a difficult diagnosis. Sao Paulo Med. J. 115(3) (1997)
Saleh, A.: Classification and diagnostic criteria in systemic vasculitis. Best Practice&Research Clinical Rheumatology 19(2), 209–221 (2005)
Merck, http://www.merck.com
Cohen, A.M., Hersh, W.R.: A survey of current work in biomedical text mining. Briefings in Bioinformatics 6(1), 57–71 (2005)
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Yıldırım, P., Çeken, Ç., Çeken, K., Tolun, M.R. (2010). Clustering Analysis for Vasculitic Diseases. In: Zavoral, F., Yaghob, J., Pichappan, P., El-Qawasmeh, E. (eds) Networked Digital Technologies. NDT 2010. Communications in Computer and Information Science, vol 88. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14306-9_5
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DOI: https://doi.org/10.1007/978-3-642-14306-9_5
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