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

Wireless Personal Communications

Volumn 102, Issue 3, 2018, Pages 2099-2116

  Manogaran, Gunasekaran ^a   Vijayakumar, V ^b   Varatharajan, R ^c   Malarvizhi Kumar, Priyan ^a   Sundarasekar, Revathi ^d   Hsu, Ching Hsien ^e  

^a VIT UNIVERSITY   (India)

^b VIT UNIVERSITY   (India)

^c Sri Ramanujar Engineering College   (India)

^d Priyadarshini Engineering College   (India)

^e CHUNG HUA UNIVERSITY   (Taiwan)

Author keywords

Bayesian hidden Markov model; Big Data; Comparative genomic hybridization; DNA copy number change; Gaussian mixture clustering; Machine learning

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER GENERATED HOLOGRAPHY; DATA HANDLING; DIAGNOSIS; DISEASES; DNA; GENES; HIDDEN MARKOV MODELS; LEARNING SYSTEMS; TRELLIS CODES;

BAYESIAN; CHANGE DETECTION ALGORITHMS; COMPARATIVE GENOMIC HYBRIDIZATIONS; CONTINUOUS MONITORING; DNA COPY NUMBERS; GAUSSIAN MIXTURE CLUSTERING; NEXT-GENERATION SEQUENCING; SCALABLE MACHINE LEARNING;

BIG DATA;

EID: 85033575553     PISSN: 09296212     EISSN: 1572834X     Source Type: Journal    
DOI: 10.1007/s11277-017-5044-z     Document Type: Article

References (69)

1. Attiyeh, E. F., Diskin, S. J., Attiyeh, M. A., Mossé, Y. P., Hou, C., Jackson, E. M., et al. (2009). Genomic copy number determination in cancer cells from single nucleotide polymorphism microarrays based on quantitative genotyping corrected for aneuploidy. Genome Research, 19(2), 276–283
2. Zhao, X., Li, C., Paez, J. G., Chin, K., Jänne, P. A., Chen, T. H., et al. (2004). An integrated view of copy number and allelic alterations in the cancer genome using single nucleotide polymorphism arrays. Cancer Research, 64(9), 3060–3071
3. Lopez, D., Gunasekaran, M., Murugan, B. S., Kaur, H., & Abbas, K. M. (2014). Spatial big data analytics of influenza epidemic in Vellore, India. In 2014 IEEE international conference on big data (Big Data) (pp. 19–24)
4. Varatharajan, R., Manogaran, G., Priyan, M. K., & Sundarasekar, R. (2017). Wearable sensor devices for early detection of Alzheimer disease using dynamic time warping algorithm. Cluster Computing, 1–10
5. Varatharajan, R., Manogaran, G., Priyan, M. K., Balaş, V. E., & Barna, C. (2017). Visual analysis of geospatial habitat suitability model based on inverse distance weighting with paired comparison analysis. Multimedia Tools and Applications, 1–21
6. Thota, C., Sundarasekar, R., Manogaran, G., Varatharajan, R., & Priyan, M. K. (2018). Centralized fog computing security platform for IoT and cloud in healthcare system. In Exploring the convergence of big data and the internet of things (pp. 141–154). IGI Global
7. Varatharajan, R., Vasanth, K., Gunasekaran, M., Priyan, M., & Gao, X. Z. (2017). An adaptive decision based kriging interpolation algorithm for the removal of high density salt and pepper noise in images. Computers & Electrical Engineering
8. Manogaran, G., Lopez, D., Thota, C., Abbas, K. M., Pyne, S., & Sundarasekar, R. (2017). Big data analytics in healthcare internet of things. In G. S. Tomar (Ed.), Innovative healthcare systems for the 21st century (pp. 263–284). Berlin: Springer
9. Manogaran, G., & Lopez, D. (2017). Spatial cumulative sum algorithm with big data analytics for climate change detection. Computers & Electrical Engineering
10. Manogaran, G., & Lopez, D. (2017). A Gaussian process based big data processing framework in cluster computing environment. Cluster Computing, 1–16
11. Campbell, P. J., Yachida, S., Mudie, L. J., Stephens, P. J., Pleasance, E. D., Stebbings, L. A., et al. (2010). The patterns and dynamics of genomic instability in metastatic pancreatic cancer. Nature, 467(7319), 1109–1113
12. Vayena, E., Salathé, M., Madoff, L. C., & Brownstein, J. S. (2015). Ethical challenges of big data in public health. PLoS Computational Biology, 11(2), e1003904
13. Lopez, D., & Gunasekaran, M. (2015). Assessment of vaccination strategies using fuzzy multi-criteria decision making. In Proceedings of the Fifth International Conference on Fuzzy and Neuro Computing (FANCCO-2015) (pp. 195–208). Berlin: Springer
14. Lopez, D., & Sekaran, G. (2016). Climate change and disease dynamics-a big data perspective. International Journal of Infectious Diseases, 45, 23–24
15. Lopez, D., & Manogaran, G. (2016). Big data architecture for climate change and disease dynamics. In G. S. Tomar et al. (Eds.) The human element of big data: issues, analytics, and performance (pp. 301–331). Boca Raton: CRC Press
16. Manogaran, G., Thota, C., & Kumar, M. V. (2016). MetaCloud data storage architecture for big data security in cloud computing. Procedia Computer Science, 87, 128–133
17. Manogaran, G., & Lopez, D. (2016). Health data analytics using scalable logistic regression with stochastic gradient descent. International Journal of Advanced Intelligence Paradigms, 9, 1–15
18. Manogaran, G., & Lopez, D. (2017). Disease surveillance system for big climate data processing and dengue transmission. International Journal of Ambient Computing and Intelligence, 8(2), 88–105
19. Thota, C., Manogaran, G., Lopez, D., & Vijayakumar, V. (2017). Big data security framework for distributed cloud data centers. In Cybersecurity breaches and issues surrounding online threat protection (pp. 288–310). IGI Global
20. Manogaran, G., Thota, C., Lopez, D., Vijayakumar, V., Abbas, K. M., & Sundarsekar, R. (2017). Big data knowledge system in healthcare. In C. Bhatt, N. Dey & A. Ashour (Eds.), Internet of things and big data technologies for next generation healthcare (pp. 133–157). Berlin: Springer
21. Gijzen, H. (2013). Development: big data for a sustainable future. Nature, 502(7469), 38
22. Wang, X., & Sun, Z. (2013). The design of water resources and hydropower cloud GIS platform based on big data. In Y. Xie, X. Cui & F. Bian (Eds.), Geo-informatics in resource management and sustainable ecosystem (pp. 313–322). Berlin: Springer
23. Howe, D., Costanzo, M., Fey, P., Gojobori, T., Hannick, L., Hide, W., et al. (2008). Big data: The future of biocuration. Nature, 455(7209), 47–50
24. Hampton, S. E., Strasser, C. A., Tewksbury, J. J., Gram, W. K., Budden, A. E., Batcheller, A. L., et al. (2013). Big data and the future of ecology. Frontiers in Ecology and the Environment, 11(3), 156–162
25. Jang, S. M., & Hart, P. S. (2015). Polarized frames on—climate change‖ and—global warming‖ across countries and states: evidence from twitter big data. Global Environmental Change, 32, 11–17
26. Zhao, W., Ma, H., & He, Q. (2009). Parallel k-means clustering based on mapreduce. In M. G. Jaatun, G. Zhao & C. Rong (Eds.), Cloud computing (pp. 674–679). Berlin: Springer
27. Nguyen, C. D., Nguyen, D. T., & Pham, V. H. (2013). Parallel two-phase K-means. In B. Murgante, S. Misra & M. Carlini (Eds.), Computational Science and Its Applications–ICCSA 2013 (pp. 224–231). Berlin: Springer
28. Sun, Z., & Fox, G. (2012). Study on parallel SVM based on MapReduce. In Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA) (p. 1). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp)
29. Ester, M., Kriegel, H. P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Kdd (Vol. 96, No. (34), pp. 226–231)
30. Li, L., & Xi, Y. (2011).Research on clustering algorithm and its parallelization strategy. In IEEE international conference on computational and information sciences (ICCIS) (pp. 325–328)
31. He, Y., Tan, H., Luo, W., Mao, H., Ma, D., Feng, S., & Fan, J. (2011). Mr-dbscan: An efficient parallel density-based clustering algorithm using mapreduce. In IEEE 17th international conference on parallel and distributed systems (ICPADS) (pp. 473–480)
32. Fries, S., Wels, S., & Seidl, T. (2014).Projected clustering for huge data sets in MapReduce. In EDBT (pp. 49–60)
33. Moise, G., Sander, J., & Ester, M. (2006). P3C: A robust projected clustering algorithm. In IEEE sixth international conference on data mining, 2006. ICDM’06 (pp. 414–425)
34. Gao, Z., Bu, W., Zheng, Y., & Wu, X. (2017). Automated layer segmentation of macular OCT images via graph-based SLIC superpixels and manifold ranking approach. Computerized Medical Imaging and Graphics, 55, 42–53
35. Baran, U., Zhu, W., Choi, W. J., Omori, M., Zhang, W., Alkayed, N. J., et al. (2016). Automated segmentation and enhancement of optical coherence tomography-acquired images of rodent brain. Journal of Neuroscience Methods, 270, 132–137
36. Li, D., Taniguchi, E. V., Cai, S., Paschalis, E. I., Wang, H., Miller, J. B., & Shen, L. Q. (2016). Comparison of swept-source and enhanced depth imaging spectral-domain optical coherence tomography in quantitative characterisation of the optic nerve head. British Journal of Ophthalmology, bjophthalmol-2016
37. Tang, J., Liu, X., & Sun, Q. (2009). A direct image contrast enhancement algorithm in the wavelet domain for screening mammograms. IEEE Journal of Selected Topics in Signal Processing, 3(1), 74–80
38. Li, C., Wang, X., Eberl, S., Fulham, M., & Feng, D. (2013). A new energy framework with distribution descriptors for image segmentation. IEEE Transactions on Image Processing, 22(9), 3578–3590
39. Vermeer, K. A., van der Schoot, J., Lemij, H. G., & de Boer, J. F. (2012). RPE-normalized RNFL attenuation coefficient maps derived from volumetric OCT imaging for glaucoma assessment RNFL attenuation coefficient maps for Glaucoma. Investigative Ophthalmology & Visual Science, 53(10), 6102–6108
40. Ma, Z., Xue, J. H., Leijon, A., Tan, Z. H., Yang, Z., & Guo, J. (2016). Decorrelation of neutral vector variables: Theory and applications. IEEE transactions on neural networks and learning systems
41. Ma, Z., Teschendorff, A. E., Leijon, A., Qiao, Y., Zhang, H., & Guo, J. (2015). Variational bayesian matrix factorization for bounded support data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(4), 876–889
42. Ng, P. A. A. Y. (2005). Learning first-order Markov models for control. In Advances in neural information processing systems 17: Proceedings of the 2004 conference (Vol. 17, p. 1). MIT Press
43. Ma, Z., Rana, P. K., Taghia, J., Flierl, M., & Leijon, A. (2014). Bayesian estimation of Dirichlet mixture model with variational inference. Pattern Recognition, 47(9), 3143–3157
44. Ma, Z., Xie, J., Li, H., Sun, Q., Si, Z., Zhang, J., & Guo, J. (2017). The role of data analysis in the development of intelligent energy networks. arXiv preprint arXiv:1705.11132
45. Ghahramani, Z. (2001). An introduction to hidden Markov models and Bayesian networks. International Journal of Pattern Recognition and Artificial Intelligence, 15(01), 9–42
46. Stanke, M., & Waack, S. (2003). Gene prediction with a hidden Markov model and a new intron submodel. Bioinformatics, 19(suppl 2), ii215–ii225
47. Henderson, J., Salzberg, S., & Fasman, K. H. (1997). Finding genes in DNA with a hidden Markov model. Journal of Computational Biology, 4(2), 127–141
48. Wang, K., Li, M., Hadley, D., Liu, R., Glessner, J., Grant, S. F., et al. (2007). PennCNV: an integrated hidden Markov model designed for high-resolution copy number variation detection in whole-genome SNP genotyping data. Genome Research, 17(11), 1665–1674
49. Boys, R. J., Henderson, D. A., & Wilkinson, D. J. (2000). Detecting homogeneous segments in DNA sequences by using hidden Markov models. Applied Statistics, 49, 269–285
50. Leroux, B. G. (1992). Maximum-likelihood estimation for hidden Markov models. Stochastic processes and their applications, 40(1), 127–143
51. Hidden Markov model. (2017). En.wikipedia.org. Retrieved October 9, 2017, from http://en.wikipedia.org/wiki/Hidden_Markov_model#/media/File:HiddenMarkovModel.svg
52. Siepel, A., & Haussler, D. (2004). Combining phylogenetic and hidden Markov models in biosequence analysis. Journal of Computational Biology, 11(2–3), 413–428
53. Krogh, A., Brown, M., Mian, I. S., Sjölander, K., & Haussler, D. (1994). Hidden Markov models in computational biology: Applications to protein modeling. Journal of Molecular Biology, 235(5), 1501–1531
54. Churchill, G. A. (1989). Stochastic models for heterogeneous DNA sequences. Bulletin of Mathematical Biology, 51(1), 79–94
55. Stanke, M., Schöffmann, O., Morgenstern, B., & Waack, S. (2006). Gene prediction in eukaryotes with a generalized hidden Markov model that uses hints from external sources. BMC Bioinformatics, 7(1), 62
56. Yada, T., Totoki, Y., Ishikawa, M., Asai, K., & Nakai, K. (1998). Automatic extraction of motifs represented in the hidden Markov model from a number of DNA sequences. Bioinformatics, 14(4), 317–325
57. Jablonowski, K. (2017). Hidden Markov models for protein domain homology identification and analysis. SH2 Domains: Methods and Protocols, 1555, 47–58
58. Lehmann, T., & Schlattmann, P. (2017). Treatment of nonignorable missing data when modeling unobserved heterogeneity with finite mixture models. Biometrical Journal, 59(1), 159–171
59. Prakash, R. M., & Kumari, R. S. S. (2017). Spatial fuzzy C means and expectation maximization algorithms with bias correction for segmentation of MR brain images. Journal of Medical Systems, 41(1), 15
60. Mihlin, A., & Levin, C. S. (2017). An expectation maximization method for joint estimation of emission activity distribution and photon attenuation map in PET. IEEE Transactions on Medical Imaging, 36(1), 214–224
61. Bhadra, A. (2017). An expectation–maximization scheme for measurement error models. Statistics & Probability Letters, 120, 61–68
62. Kounades-Bastian, D., Girin, L., Alameda-Pineda, X., Gannot, S., & Horaud, R. (2017). An EM algorithm for joint source separation and diarisation of multichannel convolutive speech mixtures. In IEEE International Conference on Acoustics, Speech and Signal Processing
63. Borges, P. (2017). EM algorithm-based likelihood estimation for a generalized Gompertz regression model in presence of survival data with long-term survivors: an application to uterine cervical cancer data. Journal of Statistical Computation and Simulation, 87, 1–11
64. Chen, F., Agüero, J. C., Gilson, M., Garnier, H., & Liu, T. (2017). EM-based identification of continuous-time ARMA Models from irregularly sampled data. Automatica, 77, 293–301
65. Shinmura, K., Kato, H., Kawanishi, Y., Yoshimura, K., Igarashi, H., Goto, M., et al. (2017). Reduced expression of the DNA glycosylase gene MUTYH is associated with an increased number of somatic mutations via a reduction in the DNA repair capacity in prostate adenocarcinoma. Molecular Carcinogenesis, 56(2), 781–788
66. Papastamoulis, P., & Rattray, M. (2017). A Bayesian model selection approach for identifying differentially expressed transcripts from RNA sequencing data. Journal of the Royal Statistical Society: Series C (Applied Statistics)
67. Killick, R., Eckley, I. A., Jonathan, P., & Chester, U. K. (2011). Efficient detection of multiple changepoints within an oceano-graphic time series. In Proceedings of the 58th world science congress of ISI
68. Scott, A. J., & Knott, M. (1974). A cluster analysis method for grouping means in the analysis of variance. Biometrics, 30, 507–512
69. Auger, I. E., & Lawrence, C. E. (1989). Algorithms for the optimal identification of segment neighborhoods. Bulletin of Mathematical Biology, 51(1), 39–54