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ReserarchID
CSTNWSH476C
Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
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This paper is largely devoted for building a novel approach which is able to explain biological phenomena like splicing, promoter gene identification, disease and disorder identification, and to acquire and exploit biological data. This paper also presents an overview on the artificial neural network based computational intelligence technique to infer and analyze biological information from wide spectrum of complex problems .Bioinformatics and computational intelligence are new research area which integrates many core subjects such as chemistry, biology, medical science, mathematics, com-puter and information science. Since most of the problems in bioinformatics are inherently hard, ill defined and possesses overlapping boundaries. Neural networks have proved to be effective in solving those problems where conventional com-putation tools failed to provide solution. Our experiments demonstrate the endeavor of biological phenomena as an effec-tive description for many intelligent applications. Having a computational tool to predict genes and other meaningful in-formation 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.
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