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 |
Splicing, Promoter Gene, Bioinformatics, Biological Disorder, Artificial Neural Networks
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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
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
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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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}
}
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 -
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