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Gene clustering with hidden Markov model optimized by PSO algorithm

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Abstract

Gene clustering is one of the most important problems in bioinformatics. In the sequential data clustering, hidden Markov models (HMMs) have been widely used to find similarity between sequences, due to their capability of handling sequence patterns with various lengths. In this paper, a novel gene clustering scheme based on HMMs optimized by particle swarm optimization algorithm is introduced. In this approach, each gene sequence is described by a specific HMM, and then for each model, its probability to generate individual sequence is evaluated. A hierarchical clustering algorithm based on a new definition of a distance measure has been applied to find the best clusters. Experiments carried out on lung cancer-related genes dataset show that the proposed approach can be successfully utilized for gene clustering.

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References

  1. http://healthfinder.gov/orgs/HR3150.htm. visited Nov 2011

  2. Krogh A, Brown M, Mian I.S, Sjolander K, Haussler D (1993) Hidden Markov models in computational biology: application to protein modeling. UCSC-CRL-93-32

  3. Zhang ZY, Li T, Ding C, Ren XW, Zhang XS (2010) Binary matrix factorization for analyzing gene expression data. Data Min Knowl Discov 20:28–52

    Article  MathSciNet  Google Scholar 

  4. Vignes M, Forbes F (2009) Gene clustering via integrated Markov models combining individual and pairwise features. IEEE/ACM Trans Comput Biol Bioinform 6:260–270

    Article  Google Scholar 

  5. Durbin R, Eddy SR, Krogh A, Mitchison G (1998) Biological sequence analysis. Cambridge University Press, Cambridge

    Book  Google Scholar 

  6. Rabiner LR (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE 77:257–286

    Article  Google Scholar 

  7. Rabiner LR, Lee CH, Juang BH, Wilpon JG (1989) HMM clustering for connected word recognition. In: Proceedings of IEEE ICASSP, pp 405–408

  8. Lee KF (1990) Context-dependent phonetic hidden Markov models for speaker-independent continuous speech recognition. IEEE Trans Acoust Speech Signal Process 38:599–609

    Article  Google Scholar 

  9. Al-Hajj R, Mokbel C, Likforman-Sulem L (2007) Combination of HMM-based classifiers for the recognition of arabic handwritten words. In: 9th International conference on document analysis and recognition, pp 959–963

  10. Panuccio A, Bicego M, Murino V (2002) A hidden Markov model-based approach to sequential data clustering. Struct Synt Stat Pattern Recognit 2396:734–743

    MATH  Google Scholar 

  11. Bicego M, Murino V, Figueiredo MAT (2004) Similarity-based classification of sequences using hidden Markov models. Pattern Recognit Soc 37:2281–2291

    Article  Google Scholar 

  12. Li C, Biswas G (2000) A Bayesian approach to temporal data clustering using hidden Markov models. In: Proceedings of the 17th international conference on machine learning, pp 543–550

  13. Ferles C, Stafylopatis A (2008) Sequence clustering with the self-organizing hidden Markov model map. In: 8th IEEE international conference on bioinformatics and bioengineering, pp 1–7

  14. Mesa A, Basterrech S, Guerberoff G, Alveraz-Valin F (2015) Hidden Markov models for gene sequence classification. Pattern Anal Appl 19:793–805

    Article  Google Scholar 

  15. Kennedy J, Eberhart RC (1995) Particle swarm optimization. Process IEEE Int Conf Neural Netw 4:1942–1948

    Article  Google Scholar 

  16. Goldberg DE, Holland JH (1988) Genetic algorithms and machine learning. Mach Learn 3:95–99

    Article  Google Scholar 

  17. Angeline PJ (1998) Evolutionary optimization versus particle swarm optimization: philosophy and performance differences. Evolut Program VII 1447:601–610

    Google Scholar 

  18. Xue L, Yin J, Ji Z, Jiang L (2006) A particle swarm optimization for hidden Markov model training. In: Proceedings of 8th international conference on signal processing

  19. Banu PK, Andrews S (2015) Gene clustering using metaheuristic optimization algorithms. Int J Appl Metaheur Comput 6(4):14–38

    Article  Google Scholar 

  20. Theodoridis S, Koutroumbas K (1999) Pattern recognition. Academic Press, Cambridge

    MATH  Google Scholar 

Download references

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Authors and Affiliations

  1. Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran

    Mohammad Soruri & S. Hamid Zahiri

  2. Department of Computer Science & Software Engineering, Faculty of Engineering and Computer Science, Concordia University, Montreal, Quebec, Canada, H3G 1M8, Montreal, QC, Canada

    Javad Sadri

  3. Ferdows Faculty of Engineering, University of Birjand, Birjand, Iran

    Mohammad Soruri

Authors
  1. Mohammad Soruri
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  2. Javad Sadri
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  3. S. Hamid Zahiri
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Correspondence to Mohammad Soruri.

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Soruri, M., Sadri, J. & Zahiri, S.H. Gene clustering with hidden Markov model optimized by PSO algorithm. Pattern Anal Applic 21, 1121–1126 (2018). https://doi.org/10.1007/s10044-018-0680-9

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  • DOI: https://doi.org/10.1007/s10044-018-0680-9

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