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Neural Network Models with Cognitive Inputs for the Detection of Rare Events in Dna Repeat Sequences


Affiliations
1 Bharathidasan University,Tiruchirappalli–620024.
2 Department of I.T. & Applications, Shrimati Indira Gandhi College, Tiruchirappalli–620002.
3 Post Graduate and Research Department of Physics, National College (Autonomous), Tiruchirappalli–620001.
4 Department of Computer Applications, Shrimati Indira Gandhi College, Tiruchirapalli–620 002.
     

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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, neural networks can be used as tools. Providing suitably preprocessed input data to the networks 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 and the results are provided. It is found that the network is capable of rapid cognition and as well gives relatively better detection performance when network parameters are tuned on the basis of cognitive inputs. The performances of the different network architectures are also compared.

Keywords

Knowledge Driven Artificial Neural Networks, Dna Sequences, Numerical Characterization, Skewness
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  • Neural Network Models with Cognitive Inputs for the Detection of Rare Events in Dna Repeat Sequences

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Authors

K. Meena
Bharathidasan University,Tiruchirappalli–620024.
K. Menaka
Department of I.T. & Applications, Shrimati Indira Gandhi College, Tiruchirappalli–620002.
T.V. Sundar
Post Graduate and Research Department of Physics, National College (Autonomous), Tiruchirappalli–620001.
K.R. Subramanian
Department of Computer Applications, Shrimati Indira Gandhi College, Tiruchirapalli–620 002.

Abstract


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, neural networks can be used as tools. Providing suitably preprocessed input data to the networks 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 and the results are provided. It is found that the network is capable of rapid cognition and as well gives relatively better detection performance when network parameters are tuned on the basis of cognitive inputs. The performances of the different network architectures are also compared.

Keywords


Knowledge Driven Artificial Neural Networks, Dna Sequences, Numerical Characterization, Skewness

References