On the Investigation of Biological Phenomena through Computational Intelligence

Jyotsana Pandey; B. K. Tripathi

doi:doi:10.11648/j.cbb.20140202.11

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On the Investigation of Biological Phenomena through Computational Intelligence

Jyotsana Pandey, B. K. Tripathi

Published in Computational Biology and Bioinformatics (Volume 2, Issue 2)

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), 2024. Published by Science Publishing Group

Keywords

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

References

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[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.
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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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TY  - JOUR
T1  - On the Investigation of Biological Phenomena through Computational Intelligence
AU  - Jyotsana Pandey
AU  - B. K. Tripathi
Y1  - 2014/04/20
PY  - 2014
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T2  - Computational Biology and Bioinformatics
JF  - Computational Biology and Bioinformatics
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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

Jyotsana Pandey

Dept. of Computer Science, Singhania University, Rajasthan, India
B. K. Tripathi

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

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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

Copy | Download

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

Copy | Download

@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}
}

Copy | Download

TY  - JOUR
T1  - On the Investigation of Biological Phenomena through Computational Intelligence
AU  - Jyotsana Pandey
AU  - B. K. Tripathi
Y1  - 2014/04/20
PY  - 2014
N1  - https://doi.org/10.11648/j.cbb.20140202.11
DO  - 10.11648/j.cbb.20140202.11
T2  - Computational Biology and Bioinformatics
JF  - Computational Biology and Bioinformatics
JO  - Computational Biology and Bioinformatics
SP  - 19
EP  - 24
PB  - Science Publishing Group
SN  - 2330-8281
UR  - https://doi.org/10.11648/j.cbb.20140202.11
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
IS  - 2
ER  -

Copy | Download