International Journal of Fuzzy Logic and Intelligent Systems

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Protein Secondary Structure Prediction using Multiple Neural Network Likelihood Models

  • Kim, Seong-Gon (CISE department, University of Florida) ;
  • Kim, Yong-Gi (Dept of Computer Science, Gyeongsang National University)
  • Received : 2010.04.05
  • Accepted : 2010.09.30
  • Published : 2010.12.25
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Abstract

Predicting Alpha-helicies, Beta-sheets and Turns of a proteins secondary structure is a complex non-linear task that has been approached by several techniques such as Neural Networks, Genetic Algorithms, Decision Trees and other statistical or heuristic methods. This project introduces a new machine learning method by combining Bayesian Inference with offline trained Multilayered Perceptron (MLP) models as the likelihood for secondary structure prediction of proteins. With varying window sizes of neighboring amino acid information, the information is extracted and passed back and forth between the Neural Net and the Bayesian Inference process until the posterior probability of the secondary structure converges.

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