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OJBTM

 Online Journal of Bioinformatics 

Volume 19 (2):107-113, 2018


Cognition enhanced artificial neural network for detection of rare events in DNA repeat sequences.

 

Meena, K1, Menaka, K2, Sundar, TV4* and Subramanian, K R3

 

a1Vice Chancellor, Bharathidasan University, 2Department of I.T. & Applications, Shrimati Indira Gandhi College,3 Department of M.C.A., Shrimati Indira Gandhi College, 4Department of Physics, National College (Autonomous), Tiruchirappalli, India.

ABSTRACT

 

Meena, K, Menaka, K, Sundar, TV, Subramanian KR., Cognition enhanced artificial neural network for detection of rare events in DNA repeat sequences Online J Bioinform, 19(2):107-113, 2011. Looking for rare variations of genetic codes between intra or inter DNA sequences is an important activity in the quest for disease identification and other related explorations. Such experiments may reveal information about variations in regular molecular structures. For the analysis of multitude of genetic sequences with the tool of neural nets, providing a suitably preprocessed input data may serve as a critical factor in the cognitive ability and processing power of the networks. Hence, an attempt has been made in this direction to construct an artificial neural network with the support of numerically characterized input data sets. We found that the network is capable of rapid cognition and as well gives relatively better detection performance when compared to conventional learning method.

 

Keywords: Knowledge driven Artificial Neural Networks, DNA sequences, Numerical Characterization, Skewness.


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