Abstract
In this paper, we propose an unsupervised learning approach with an objective to understand gene expressions for analysis of Wilson’s disease in the liver of Mus musculus organisms. We proceeded to obtain the best parameters for cluster division to correctly classify gene expression sets so as to capture the effect and characteristics of the disease in the genome levels of the organisms in the best possible way. The clustering proved beneficial in capturing the correct genetic analogy of Wilson’s disease. Analytical experiments were carried out using various clustering algorithms and were evaluated using performance metrics including silhouette score analysis and Calinski–Harabasz index.
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Compston, A.: Progressive lenticular degeneration: a familial nervous disease associated with cirrhosis of the liver, by SA Kinnier Wilson, (From the National Hospital, and the Laboratory of the National Hospital, Queen Square, London) Brain 1912: 34; 295–509. Brain 132(8), 1997–2001 (2009)
Rodriguez-Castro, K.I., Hevia-Urrutia, F.J., Sturniolo, G.C.: Wilson’s disease: a review of what we have learned. World J. Hepatol. 7(29), 2859 (2015)
Link, C.D., Taft, A., Kapulkin, V., Duke, K., Kim, S., Fei, Q., Wood, D.E., Sahagan, B.G.: Gene expression analysis in a transgenic Caenorhabditis elegans Alzheimer’s disease model. Neurobiol. Aging 24(3), 397–413 (2003)
Rajkumar, A.P., Qvist, P., Lazarus, R., Lescai, F., Jia, J., Nyegaard, M., Mors, O., Børglum, A.D., Li, Q., Christensen, J.H.: Experimental validation of methods for differential gene expression analysis and sample pooling in RNA-seq. BMC Genom. 16(1), 548 (2015)
Matarin, M., Salih, D.A., Yasvoina, M., Cummings, D.M., Guelfi, S., Liu, W., Nahaboo Solim, M.A., et al.: A genome-wide gene-expression analysis and database in transgenic mice during development of amyloid or tau pathology. Cell Rep. 10(4), 633–644 (2015)
Khondoker, M., Dobson, R., Skirrow, C., Simmons, A., Stahl, D.: A comparison of machine learning methods for classification using simulation with multiple real data examples from mental health studies. Stat. Methods Med. Res. 25(5), 1804–1823 (2016)
Mamoshina, P., Volosnikova, M., Ozerov, I.V., Putin, E., Skibina, E., Cortese, F., Zhavoronkov, A.: Machine learning on human muscle transcriptomic data for biomarker discovery and tissue-specific drug target identification. Front. Genetics 9 (2018)
Huang, X., Liu, H., Li, X., Guan, L., Li, J., Tellier, L.C.A.M., Yang, H., Wang, J., Zhang, J.: Revealing Alzheimer’s disease genes spectrum in the whole-genome by machine learning. BMC Neurol. 18(1), 5 (2018)
MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, no. 14, pp. 281–297 (1967)
Johnson, S.C.: Hierarchical clustering schemes. Psychometrika 32(3), 241–254 (1967)
D’Andrade, R.G.: U-statistic hierarchical clustering. Psychometrika 43(1), 59–67 (1978)
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Misra, D., Tiwari, A., Chaturvedi, A. (2019). Large-Scale Meta-Analysis of Genes Encoding Pattern in Wilson’s Disease. In: Bhatia, S., Tiwari, S., Mishra, K., Trivedi, M. (eds) Advances in Computer Communication and Computational Sciences. Advances in Intelligent Systems and Computing, vol 924. Springer, Singapore. https://doi.org/10.1007/978-981-13-6861-5_34
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DOI: https://doi.org/10.1007/978-981-13-6861-5_34
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