Abstract
The field of Omics [1] has produced a large amount of research data, which is desirable for processing and estimating the discriminant classes and disordered sequences, usually the gene and protein play an vital role in controlling the biological process of the human body, with the use of genic data one can easily able to find the mutated gene causing disease and by the use of protein data the intrinsic disorder protein causing defective parts activity can be traced out. This paper brings out the soft computational machine learning research efforts in the genomic [2] and proteomic [3] data, thus providing easier machine intelligence disease classifier [4] with discriminant feature selection. Then the disease features are effective in selecting the optimal disorder enzyme causing protein [5], so that the relevant biological process activities [6] affected due to the various protein enzyme causing effects can be effectively comprehended.
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References
Vanitha, D., Devaraj, D., Venkatesulu, M.: Gene expression data classification using support vector machine and mutual information-based gene selection. Proc. Comp. Sci. 47, 13–21 (2015). Elsevier
Chanchal, K., Matthias, M.: Bioinformatics analysis of mass spectrometry-based proteomics data sets. FEBS Let. 583, 1703–1712 (2009). Elsevier
Maji, P., Paul, S.: Scalable Pattern Recognition Algorithms: Applications in Computational Biology and Bioinformatics. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-05630-2. 22
Gunavathi, C., Premalath, K.: Performance analysis of genetic algorithm with KNN and SVM for feature selection in tumour classification. Int. J. Comput. Control. Quant. Inf. Eng. 8, 1397–1404 (2014)
Kristain, O., Marko, L., Sampsa, H.: Fast gene ontology based clustering for microarray experiments. Bio-Data Min. 01, 1–8 (2008). Bio-Med Central Ltd
Jianzhen, J., Yongjin, K.: Discovering disease-genes by topological features in human protein-protein interaction network. Bioinform. Sys. Biol. 22, 2800–2805 (2007)
Mundra, P.A., Rajapakse, J.C.: SVM-RFE with MRMR filter for gene selection. IEEE Trans. Nanobiosci. 9, 31–37 (2010)
Ganeshkumar, P., Victoire, T.A.A., Renukadevi, P.: Design of fuzzy expert system for microarray data classification using a novel genetic swarm algorithm. Expert Syst. Appl. 39, 1811–1821 (2012)
Wasikowski, M., Chen, X.: Combating the small class imbalance problem using feature selection. IEEE Trans. Knowl. Data Eng. 22, 1388–1400 (2010)
Chawla, N., Japkowicz, N., Kotcz, A.: Editorial: special issue on learning from imbalanced datasets. ACM SIGKDD Explor. Newsl. 6, 1–6 (2004)
Zheng, Z., Wu, X., Srihari, R.: Feature selection for text categorization on imbalanced data. ACM SIGKDD Explor. Newsl. 6, 80–89 (2004)
Kotsiantis, S., Kanellopoulos, D., Pintelas, P.: Handling imbalanced datasets: a review. GESTS Int. Trans. Comput. Sci. Eng. 30, 15–23 (2006)
Visa, S., Ralescu, A.: The effect of imbalanced data class distribution on fuzzy classifiers - experimental study. In: FUZZIEEE 2005, Reno, Nevada, USA, vol. 5, pp. 749–754. IEEE, Nevada (2005)
Weiss, G., Provost, F.: Learning when training data are costly: the effect of class distribution on tree induction. J. Artif. Intell. Res. 19, 315–354 (2003)
Han, J., Kamber, M.: Data Mining Concepts and Techniques. Morgan Kaufmann, San Francisco (2006)
Ben-Dor, A., Bruhn, L., Friedman, N., Nachman, I., Schummer, M., Yakhini, Z.: Tissue classification with gene expression profiles. J. Comput. Biol. 7, 559–584 (2000)
Mitchell, T.: Machine Learning. McGraw Hill, New York (1997)
Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27, 861–874 (2006)
National Center for Biotechnology Information. http://www.ncbi.nlm.nih.gov
Bell, J.B.B., Kumar, P.G.: Using continuous feature selection metrics to suppress the class imbalance problem. Int. J. Sci. Eng. Res. 3, 27–35 (2012)
Alpaydin, E.: An Introduction to Machine Learning. The MIT Press, Massachusetts (2004)
Acknowledgments
I give my sincere thanks to my research guide and my fellow research students, for their high motivation towards this work. And I thank the NICHE research center for continuing the research work and finally I thank God for providing through a good parental support.
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Briso Becky Bell, J., Maria Celestin Vigila, S. (2018). Genic Disorder Identification and Protein Analysis Using Soft Computing Methods. In: Zelinka, I., Senkerik, R., Panda, G., Lekshmi Kanthan, P. (eds) Soft Computing Systems. ICSCS 2018. Communications in Computer and Information Science, vol 837. Springer, Singapore. https://doi.org/10.1007/978-981-13-1936-5_1
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