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188宝金博页面版:  Machine Learning Based Big Data Processing Framework for Cancer Diagnosis Using Hidden Markov Model and GM Clustering

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内容提示： Machine Learning Based Big Data ProcessingFramework for Cancer Diagnosis Using Hidden MarkovModel and GM ClusteringGunasekaran Manogaran 1? V. Vijayakumar 2 ?R. Varatharajan 3 ? Priyan Malarvizhi Kumar 1 ?Revathi Sundarasekar 4 ? Ching-Hsien Hsu 5? Springer Science+Business Media, LLC, part of Springer Nature 2017Abstract The change in the DNA is a form of genetic variation in the human genome. Inaddition, the DNA copy number change is also linked with the progression of manyemerging diseases. Array-b...

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Machine Learning Based Big Data ProcessingFramework for Cancer Diagnosis Using Hidden MarkovModel and GM ClusteringGunasekaran Manogaran 1• V. Vijayakumar 2 •R. Varatharajan 3 • Priyan Malarvizhi Kumar 1 •Revathi Sundarasekar 4 • Ching-Hsien Hsu 5? Springer Science+Business Media, LLC, part of Springer Nature 2017Abstract The change in the DNA is a form of genetic variation in the human genome. Inaddition, the DNA copy number change is also linked with the progression of manyemerging diseases. Array-based Comparative Genomic Hybridization (CGH) is consideredas a major task when measuring the DNA copy number change across the genome.Moreover, DNA copy number change is an essential measure to diagnose the cancerdisease. Next generation sequencing is an important method for studying the spread ofinfectious disease qualitatively and quantitatively. CGH is widely used in continuousmonitoring of copy number of thousands of genes throughout the genome. In recent years,the size of the DNA sequence data is very large. Hence, there is a need to use a scalablemachine learning approach to overcome the various issues in DNA copy number changedetection. In this paper, we use a Bayesian hidden Markov model (HMM) with GaussianMixture (GM) Clustering approach to model the DNA copy number change across thegenome. The proposed Bayesian HMM with GM Clustering approach is compared withvarious existing approaches such as Pruned Exact Linear Time method, binary segmen-tation method and segment neighborhood method. Experimental results demonstrate theeffectiveness of our proposed change detection algorithm.Keywords Big Data ? Machine learning ? Bayesian hidden Markov model ? Gaussianmixture clustering ? DNA copy number change ? Comparative genomichybridization& Gunasekaran Manogarangunavit@gmail.com1VIT University, Vellore, India2School of Computing Science and Engineering, VIT University, Chennai, Tamil Nadu, India3Sri Ramanujar Engineering College, Chennai, India4Priyadarshini Engineering College, Vellore, India5Chung Hua University, Hsinchu, Taiwan123Wireless Pers CommunDOI 10.1007/s11277-017-5044-z

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188宝金博页面版:  Machine Learning Based Big Data Processing Framework for Cancer Diagnosis Using Hidden Markov Model and GM Clustering

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188宝金博页面版: Machining of biocompatible materials： a review

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