Abstract
Emerging patterns (EP) represent a class of interaction structures and have recently been proposed as a tool for data mining. Especially, EP have been applied to the production of new types of classifiers during classification in data mining. Traditional clustering and pattern mining algorithms are inadequate for handling the analysis of high dimensional gene expression data or the analysis of multi-source data based on the same variables (e.g. genes), and the experimental results are not easy to understand. In this paper, a simple scheme for using EP to improve the performance of classification procedures in multi-source data is proposed. Also, patterns that make multi-source data easy to understand are obtained as experimental results. A new method for producing EP based on observations (e.g. samples in microarray data) in the search of classification patterns and the use of detected patterns for the classification of variables in multi-source data are presented.
This work was supported by the Brain Korea 21 Project in 2004.
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References
Barabasi, A.L.: Link, Penguin (2003)
Boulesteix, A.L., Tutz, G., Strimmer, K.: A CART-based approach to discover emerging patterns in microarray data. Bioinformatics 19, 2465–2472 (2003)
Boser, B.E., Guyon, I.M., Vapnik, V.: A training algorithm for optimal margin classifiers. In: Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory, vol. 5, pp. 144–152 (1992)
Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2, 121–167 (1998)
Dong, G., Li, J.: Efficient mining of emerging patterns: discovering trends and differences. In: Proceedings of the SIGKDD (5th ACM International Conference on Knowledge Discovery and Data Mining), vol. 5, pp. 43–52 (1999)
DeRisi, J., Iyer, V., Brown, P.: Exploring the metabolic and genetic control of gene expression on a genomic scale. Science 278, 680–686 (1997)
Li, J., Wong, L.: Emerging patterns and gene expression data. In: Proceedings of 12th Workshop on Genome Informatics, vol. 12, pp. 3–13 (2001)
Xia, L., Shaoqi, R., Yadong, W., Binsheng, G.: Gene mining: a novel and powerful ensemble decision approach to hunting for disease genes using microarray expression profiling. Nucleic Acids Research 32, 2685–2694 (2004)
Eisen, M., Spellman, P., Brown, P., Botstein, D.: Cluster analysis and display of genome-wide expression patterns. In: Proceedings of the National Academy of Sciences of the United States of America, vol. 95, pp. 14863–14868 (1998)
Brown, M.P.S., Grundy, W.N., Lin, D., Cristianini, N., Sugnet, C., Furey, T., Ares, J.M., Haussler, D.: Knowledge-base analysis of microarray gene expression data using support vector machines. Proceedings of the National Academy of Science of the United States of America 97, 262–267 (2000)
Pellegrini, M., Marcotte, E.M., Thompson, M.J., Eisenberg, D., Yeates, T.O.: Assigning protein functions by comparative genome analysis: protein phylogenetic profiles. In: Proceedings of the National Academy of Sciences of the United States of America, vol. 96, pp. 4285–4288 (1999)
West, M., Nevins, J.R., Spang, R., Zuzan, H.: Bayesian regression analysis in the ‘large p, small n’ paradigm with application in DNA microarray studies. Technical Report 15, Institute of Statistics and Decision Sciences, Duke University (2000)
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines. Cambridge UP, Cambridge (2000)
Pavlidis, P., Weston, J., Cai, J., Grundy, W.N.: Learning gene functional classifications from multiple data types. Journal of Computational Biology 9, 401–411 (2002)
Spellman, P.T., Sherlock, G., Zhang, M.Q., Iyer, V.R., Anders, K., Eisen, M.B., Brown, P.Q., Botstein, D., Futcher, B.: Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Molecular Biology of the Cell 9, 3273–3297 (1998)
Tamayo, P., Slonim, D., Mesirov, J., Zhu, Q., Kitareewan, S., Dmitrovsky, E., Lander, E., Golub, T.: Interpreting patterns of gene expression with self-organizing maps. In: Proceedings of the National Academy of Sciences of the United States of America, vol. 96, pp. 2907–2912 (1999)
Chu, S., DeiRisi, J., Eisen, M., Mulholland, J., Botstein, D., Brown, P., Herskowitz, I.: The transcriptional program of sporulation in budding yeast. Science 282, 699–705 (1998)
Altschul, S.F., Madden, T.L., Schaffer, A.A., Zhang, Z., Miller, W., Lipman, D.J.: Gapped BLAST and PSI-BLAST: A new generation of protein database search programs. Nucleic Acids Research 25, 3389–3402 (1997)
Larray, T.H., Yu., F.-l.C., Stephen, C.F.: Using Emerging Pattern Based Projected Clustering and Gene Exptression Data for Cancer Detection. In: Proceedings of the Asia-Pacific Bioinformatics Conference, vol. 29, pp. 75–87 (2004)
Yuhang, W., Filla, M.M.: Application of Relief-F Feature Filtering Algorithm to Selecting Informative Genes for Cancer Classification Using Microarray Data. In: International IEEE Computer Society Computational Systems Bioinformatics Conference, vol. 3, pp. 497–498 (2004)
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Yoon, HS., Lee, SH., Kim, J.H. (2005). Application of Emerging Patterns for Multi-source Bio-Data Classification and Analysis. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539087_128
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DOI: https://doi.org/10.1007/11539087_128
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