Advances in Computational Sciences and Technology

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

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.
     

   Subscribe/Renew Journal


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
Subscription Login to verify subscription
User
Notifications
Font Size


  • Altun H, Bilgil A , Fidan B.C., (2007). Treatment of multi-dimensional data to enhance neural network estimators in regression problems, Expert Systems with Applications, 32, 599-605.

  • Altun, H., & Curtis, K. M. (1999). The accurate estimation of articulatory synthesiser parameters through reducing the degree of saturation in a neural network hidden layer. In International conference on phonetic science, ICPhS’99 (pp. 2263–2267).

  • Baldi, P and Beunak, S. (1998). Bioinformatics: The machine Learning Approach. MIT Press, Cambridge, MA.

  • Carpenter, W. and Barthelemy, J. (1994). Common Misconceptions about Neural Networks as Approximators. Journal of Computing in Civil Engineering, Vol. 8, No. 3: pp. 345–358.

  • Haykin, S, (1999). Neural networks: a comprehensive fundation. (2nd ed.) Upper Saddle Rever, Prentice Hall-New Jersey.

  • Jordan, M.I., (1995). "Why the logistic function? A tutorial discussion on probabilities and neural networks", MIT Computational Cognitive Science Report 9503. http://www.cs.berkeley.edu/~jordan/papers/uai.ps.Z.

  • Jurka, J., Kapitonov, V.V., Pavlicek, A., Klonowski, P., Kohany, O., Walichiewicz, J. (2005). Repbase Update, a database of eukaryotic repetitive elements. Cytogentic and Genome Research, 110, 462- 467. Repbase version 15.01, (2010). FASTA and EMBL formats. GIRI: Genetic Information Research Institute. www.Girinst.org.

  • Kusiak, A. (2000). Computational Intelligence in Design and Manufacturing, Wiley, New York.

  • Lawrence, J. and Fredrickson, J, (1998). BrainMaker User’s Guide and Reference Manual, 7th Edn., California Scientific Software, Nevada City, CA.

  • Liu Y, (2002). The Numerical Characterization and Similarity Analysis of DNA Primary Sequences. Internet Electron. J. Mol. Des. I, 675-684, http://www. Biochempress.com.

  • Meena K, Menaka K., Sundar T.V. and Subramanian K.R., (2010). “Mathematical Lensing of DNA Repeat sequences”, International Journal of Computational Intelligence in Bioinformatics, Vol. 3 No. 3, 257–265.

  • Rumelhart, D.E., Hinton, G.E., and Williams, R.J. (1986 a). Learning Internal Representations by Error Back Propagation, in D.E. Rumelhart and J.L. McClelland (eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1, Foundations, The MIT Press, Ch 8.

  • Rumelhart, D.E., G.E. Hinton, and R.J. Williams, (1986 b). Learning representations by backpropagation errors, Nature, 323, 533–536.

  • Swingler, K. (1996). Applying Neural Networks: A Practical Guide, Morgan Kaufmann, San Francisco, CA.

  • Tom D'heygere, Peter L. M. Goethals and Niels De Pauw, (2003). Use of genetic algorithms to select input variables in decision tree models for the prediction of benthic macroinvertebrates. J. Ecological Modelling .Volume 160, Issue 3, Pages 291–300.


Abstract Views: 1891

PDF Views: 2




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

Abstract Views: 1891  |  PDF Views: 2

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