Abstract
Similarity-based grouping of data entries in one or more data sources is a task underlying many different data management tasks, such as, structuring search results, removal of redundancy in databases and data integration. Similarity-based grouping of data entries is not a trivial task in the context of life science data sources as the stored data is complex, highly correlated and represented at different levels of granularity. The contribution of this paper is two-fold. 1) We propose a method for similarity-based grouping and 2) we show results from test cases. As the main steps the method contains specification of grouping rules, pairwise grouping between entries, actual grouping of similar entries, and evaluation and analysis of the results. Often, different strategies can be used in the different steps. The method enables exploration of the influence of the choices and supports evaluation of the results with respect to given classifications. The grouping method is illustrated by test cases based on different strategies and classifications. The results show the complexity of the similarity-based grouping tasks and give deeper insights in the selected grouping tasks, the analyzed data source, and the influence of different strategies on the results.
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References
Altschul, S.F., Gish, W., Miller, W., Myers, E.W., Lipman, D.J.: Basic local alignment search tool. Journal of Molecular Biology 215, 403–410 (1990)
Berg, J.M., Tymoczko, J.L., Stryer, L.: Biochemistry. W.H. Freeman and Company, New York (2002)
Bilke, A., Bleiholder, J., Böhm, C., Draba, K., Naumann, F.: Automatic Data Fusion with HumMer. In: Demo at VLDB Conference, pp. 1251–1254 (2005)
Couto, F.M., Silva, M.J., Coutinho, P.: Semantic similarity over the gene ontology: family correlation and selecting disjunctive ancestors. In: Conference on Information and Knowledge Management, pp. 343–344 (2005)
Doms, A., Schroeder, M.: GoPubMed: Exploring PubMed with the GeneOntology. Nucleic Acids Research 33, W783–W786 (2005)
Gabaldon, T., Huynen, M.A.: Prediction of protein function and pathways in the genome era. Cellular and molecular life sciences: CMLS 61(7-8), 930–944 (2004)
The Gene Ontology Consortium. Gene Ontology: tool for the unification of biology. Nature Genetics 25(1), 25–29 (2000), http://www.geneontology.org/
Herbert, K.G., Gehani, N.H., Piel, W.H., Wang, J., Wu, C.H.: BIO-AJAX: An Extensible Framework for Biological Data Cleaning. SIGMOD Record 33(2), 51–57 (2004)
Java implementation of the Smith-Waterman algorithm for biological sequence alignment, http://jaligner.sourceforge.net/
Koh, J.L.Y., Lee, M.L., Khan, A.M., Tan, P.T.J., Brusic, V.: Duplicate Detection in Biological Data using Association Rule Mining. In: ECML/PKDD Workshop on Data Mining and Text Mining for Bioinformatics, pp. 31–37 (2004)
Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions, and reversals. Soviet Physics Doklady 10(8), 707–710 (1966)
Lord, P.W., Stevens, R., Brass, A., Goble, C.A.: Investigating semantic similarity measures across the Gene Ontology: the relationship between sequence and annotation. Bioinformatics 19(10), 1275–1283 (2003)
Pfeifer, U., Poersch, T., Fuhr, N.: Searching Proper Names in Databases. In: Conference on Hypertext - Information Retrieval - Multimedia, pp. 259–275 (1995)
Shamir, R., Sharan, R.: Algorithmic Approaches to Clustering Gene Expression Data. In: Jiang, T., Smith, T., Xu, Y., Zhang, M.Q. (eds.) Current Topics in Computational Biology, pp. 269–299. MIT Press, Cambridge (2002)
Speer, N., Fröhlich, H., Spieth, C., Zell, A.: Functional Distances for Genes Based on GO Feature Maps and their Application to Clustering. In: IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), pp. 142–149 (2005)
Steinbach, M., Karypis, G., Kumar, V.: A comparison of document clustering techniques. In: KDD Workshop on Text Mining (2000)
Strehl, A.: Relationship-based Clustering and Cluster Ensembles for High-dimensional Data Mining. PhD thesis, University of Texas at Austin (2002)
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Jakonienė, V., Rundqvist, D., Lambrix, P. (2006). A Method for Similarity-Based Grouping of Biological Data. In: Leser, U., Naumann, F., Eckman, B. (eds) Data Integration in the Life Sciences. DILS 2006. Lecture Notes in Computer Science(), vol 4075. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11799511_13
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DOI: https://doi.org/10.1007/11799511_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36593-8
Online ISBN: 978-3-540-36595-2
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