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On the Investigation of Biological Phenomena through Computational Intelligence

Received: 8 January 2014     Accepted: 16 April 2014     Published: 20 April 2014
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Abstract

This paper presents the approach towards understanding and building integrative system to explain biological phenomena like splicing, promoter gene identification, disease and disorder identification through artificial neural network based computational intelligence technique. Bioinformatics and computational intelligence are new research area which integrates many core subjects such as chemistry, biology, medical science, mathematics, computer and information science. Since most of the problems in biological information processing are inherently hard, ill defined and possesses overlapping boundaries. Neural networks have proved to be effective in solving those problems where conventional computation tools failed to provide solution. Having a computational tool to predict genes and other meaningful information is therefore of great value, and can save a lot of expensive and time consuming experiments for biologists. This paper will focus on issues related to design methodology comprising neural network to analyze biological information and investigate them for powerful applications. This paper also presents an empirical evaluation on wide spectrum of complex problems to infer and analyze biological information. Our experiments demonstrate the endeavor of biological phenomena as an effective description for many intelligent applications

Published in Computational Biology and Bioinformatics (Volume 2, Issue 2)
DOI 10.11648/j.cbb.20140202.11
Page(s) 19-24
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2014. Published by Science Publishing Group

Keywords

Splicing, Promoter Gene, Bioinformatics, Biological Disorder, Artificial Neural Networks

References
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[2] Pertea M, Lin X, Salzberg S: Gene Splicer: a new computational method for splice site prediction. Nucleic Acids Res 2001, 29(5):1185-90.
[3] JSR Jang, CT Sun, E Mizutani, -“Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence Automatic Control”, IEEE …, ieeexplore.ieee.org 1997.
[4] David Reby, Sovan Lek, I Dimopoulos, “Artificial neural networks as a classification method in the behavioural sciences”, Behavioural Processes 40 (1997) 35–43, Elsevier.
[5] Pati, NN Ivanova, N Mikhailova, G Ovchinnikova GenePRIMP:” A gene prediction improvement pipeline for prokaryotic genomes” Nature …, 2010
[6] Pierre Baldi, Sren Brunak Bioinformatics: “The Machine Learning Approach”, Second Edition (Adaptive Computation and Machine Learning) on Amazon.com. 2002
[7] Zoheir Ezziane, “Applications of artificial intelligence in bioinformatics” 2006
[8] Mitra S and Hayashi Y, “Bioinformatics with soft computing”, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, Vol. 36, pp. 616-635 2006
[9] Tasoulis D.K., Plagianakos V. P., and Vrahatis M. N. Computational Intelligence Algorithms and DNA Microarrays, Studies in Computational Intelligence(SCI) 94, pp. 1-31. 2008
[10] Tasoulis D.K., Plagianakos V. P., and Vrahatis M. N. Computational Intelligence Algorithms and DNA Microarrays, Studies in Computational Intelligence(SCI) 94, pp. 1-31. 2008
[11] Arpad Kelemen Ajith Abraham Yuehui Chen (Eds.) “Computational Intelligence in Bioinformatics. Studies in Computational Intelligence”, Springer- Verlag Berlin Heidelberg, 2008
[12] C. and Reynolds, R. "Analysis of E. Coli Promoter Sequences" Nucleic Acids Research, 15:2343-2361, 1987
[13] Wu CH, McLarty JW. “Neural Networks and Genome Informatics”. Methods in computational Biology and Biochemistry, 1 ed. Vol. 1. Elsevier; 2000.
[14] Reese MG, Eeckman FH. “Novel Neural Network Prediction. Systems For Human Promoters And. Splice Sites”. 1995
[15] Mariofanna G. Milanova3, Tomasz G. Smolinski4,20. Ogiela, M. & Goodenday, L.S. "Knowledge Discovery Approach to Automated Cardiac SPECT Diagnosis" Artificial Intelligence in Medicine, vol. 23:2, pp 149-169, Oct 2001.
[16] Kenta Nakai & Minoru Kanehisa, "Expert Sytem for Predicting Protein Localization Sites in Gram-Negative Bacteria", PROTEINS: Structure, Function, and Genetics 11:95-110, 1991
[17] Aik Choon TAN and David GILBERT, “An empirical comparison of supervised machine learning techniques in bioinformatics” Bioinformatics Research Centre, Department of Computing Science 12 Lilybank Gardens, University of Glasgow, Glasgow G12 8QQ, UK 2009
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  • APA Style

    Jyotsana Pandey, B. K. Tripathi. (2014). On the Investigation of Biological Phenomena through Computational Intelligence. Computational Biology and Bioinformatics, 2(2), 19-24. https://doi.org/10.11648/j.cbb.20140202.11

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    ACS Style

    Jyotsana Pandey; B. K. Tripathi. On the Investigation of Biological Phenomena through Computational Intelligence. Comput. Biol. Bioinform. 2014, 2(2), 19-24. doi: 10.11648/j.cbb.20140202.11

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    AMA Style

    Jyotsana Pandey, B. K. Tripathi. On the Investigation of Biological Phenomena through Computational Intelligence. Comput Biol Bioinform. 2014;2(2):19-24. doi: 10.11648/j.cbb.20140202.11

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  • @article{10.11648/j.cbb.20140202.11,
      author = {Jyotsana Pandey and B. K. Tripathi},
      title = {On the Investigation of Biological Phenomena through Computational Intelligence},
      journal = {Computational Biology and Bioinformatics},
      volume = {2},
      number = {2},
      pages = {19-24},
      doi = {10.11648/j.cbb.20140202.11},
      url = {https://doi.org/10.11648/j.cbb.20140202.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cbb.20140202.11},
      abstract = {This paper presents the approach towards understanding and building integrative system to explain biological phenomena like splicing, promoter gene identification, disease and disorder identification through artificial neural network based computational intelligence technique. Bioinformatics and computational intelligence are new research area which integrates many core subjects such as chemistry, biology, medical science, mathematics, computer and information science. Since most of the problems in biological information processing are inherently hard, ill defined and possesses overlapping boundaries. Neural networks have proved to be effective in solving those problems where conventional computation tools failed to provide solution. Having a computational tool to predict genes and other meaningful information is therefore of great value, and can save a lot of expensive and time consuming experiments for biologists. This paper will focus on issues related to design methodology comprising neural network to analyze biological information and investigate them for powerful applications. This paper also presents an empirical evaluation on wide spectrum of complex problems to infer and analyze biological information. Our experiments demonstrate the endeavor of biological phenomena as an effective description for many intelligent applications},
     year = {2014}
    }
    

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    AB  - This paper presents the approach towards understanding and building integrative system to explain biological phenomena like splicing, promoter gene identification, disease and disorder identification through artificial neural network based computational intelligence technique. Bioinformatics and computational intelligence are new research area which integrates many core subjects such as chemistry, biology, medical science, mathematics, computer and information science. Since most of the problems in biological information processing are inherently hard, ill defined and possesses overlapping boundaries. Neural networks have proved to be effective in solving those problems where conventional computation tools failed to provide solution. Having a computational tool to predict genes and other meaningful information is therefore of great value, and can save a lot of expensive and time consuming experiments for biologists. This paper will focus on issues related to design methodology comprising neural network to analyze biological information and investigate them for powerful applications. This paper also presents an empirical evaluation on wide spectrum of complex problems to infer and analyze biological information. Our experiments demonstrate the endeavor of biological phenomena as an effective description for many intelligent applications
    VL  - 2
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Author Information
  • Dept. of Computer Science, Singhania University, Rajasthan, India

  • Dept. of Computer Science & Engineering, HBTI, Kanpur, India

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