Machine Learning Based Big Data Processing Framework for Cancer Diagnosis Using Hidden Markov Model and GM Clustering View Full Text


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Article Info

DATE

2017-11-13

AUTHORS

Gunasekaran Manogaran, V. Vijayakumar, R. Varatharajan, Priyan Malarvizhi Kumar, Revathi Sundarasekar, Ching-Hsien Hsu

ABSTRACT

The change in the DNA is a form of genetic variation in the human genome. In addition, the DNA copy number change is also linked with the progression of many emerging diseases. Array-based Comparative Genomic Hybridization (CGH) is considered as 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 cancer disease. Next generation sequencing is an important method for studying the spread of infectious disease qualitatively and quantitatively. CGH is widely used in continuous monitoring 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 scalable machine learning approach to overcome the various issues in DNA copy number change detection. In this paper, we use a Bayesian hidden Markov model (HMM) with Gaussian Mixture (GM) Clustering approach to model the DNA copy number change across the genome. The proposed Bayesian HMM with GM Clustering approach is compared with various existing approaches such as Pruned Exact Linear Time method, binary segmentation method and segment neighborhood method. Experimental results demonstrate the effectiveness of our proposed change detection algorithm. More... »

PAGES

2099-2116

References to SciGraph publications

  • 2013-10-02. Big data for a sustainable future in NATURE
  • 2015-11-25. Assessment of Vaccination Strategies Using Fuzzy Multi-criteria Decision Making in PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON FUZZY AND NEURO COMPUTING (FANCCO - 2015)
  • 2008-09-03. The future of biocuration in NATURE
  • 2009. Parallel K-Means Clustering Based on MapReduce in CLOUD COMPUTING
  • 1989-01. Stochastic models for heterogeneous DNA sequences in BULLETIN OF MATHEMATICAL BIOLOGY
  • 2013. Parallel Two-Phase K-Means in COMPUTATIONAL SCIENCE AND ITS APPLICATIONS – ICCSA 2013
  • 1989-01. Algorithms for the optimal identification of segment neighborhoods in BULLETIN OF MATHEMATICAL BIOLOGY
  • 2016-12-13. Spatial Fuzzy C Means and Expectation Maximization Algorithms with Bias Correction for Segmentation of MR Brain Images in JOURNAL OF MEDICAL SYSTEMS
  • 2006-02-09. Gene prediction in eukaryotes with a generalized hidden Markov model that uses hints from external sources in BMC BIOINFORMATICS
  • 2010-10-27. The patterns and dynamics of genomic instability in metastatic pancreatic cancer in NATURE
  • 2017-01-03. Big Data Knowledge System in Healthcare in INTERNET OF THINGS AND BIG DATA TECHNOLOGIES FOR NEXT GENERATION HEALTHCARE
  • 2017-06-22. Wearable sensor devices for early detection of Alzheimer disease using dynamic time warping algorithm in CLUSTER COMPUTING
  • 2013. The Design of Water Resources and Hydropower Cloud GIS Platform Based on Big Data in GEO-INFORMATICS IN RESOURCE MANAGEMENT AND SUSTAINABLE ECOSYSTEM
  • 2017-06-22. A Gaussian process based big data processing framework in cluster computing environment in CLUSTER COMPUTING
  • 2017-05-14. Big Data Analytics in Healthcare Internet of Things in INNOVATIVE HEALTHCARE SYSTEMS FOR THE 21ST CENTURY
  • 2017-01-15. Hidden Markov Models for Protein Domain Homology Identification and Analysis in SH2 DOMAINS
  • 2017-06-03. Visual analysis of geospatial habitat suitability model based on inverse distance weighting with paired comparison analysis in MULTIMEDIA TOOLS AND APPLICATIONS
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