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Manimekalai, M.
- Comparision of Prediction of Structure of Protein of Soy Beans Using Radial Basis Function Neural Networks with other Methods for Rs126 and PDB Data Sets
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Authors
K. Meena
1,
M. Manimekalai
2
Affiliations
1 Bharathidasan University, Tiruchirappalli, Tamil Nadu, IN
2 Department of MCA, Shrimati Indira Gandhi College, Tiruchirappalli, Tamil Nadu, IN
1 Bharathidasan University, Tiruchirappalli, Tamil Nadu, IN
2 Department of MCA, Shrimati Indira Gandhi College, Tiruchirappalli, Tamil Nadu, IN
Source
Journal of Computational Intelligence in Bioinformatics, Vol 6, No 1 (2013), Pagination: 49-57Abstract
In this paper Prediction of structure of Protein of Soy Beans using Radial Basis Function Neural Networks for RS126 Data set and PDB Data set has been made and compared with other traditional methods namely Chou-Fasman, GOR, APSSP, PHD, Prospect and SSpro.The training and testing sets for both have been taken into consideration to train and test the networks respectively. The major parameter for finding the accuracy of the protein secondary structure prediction is the per-residue prediction accuracy, Q3, which gives the percentage of all correctly predicted residues within the three-state (H, E, C) classes, and has also been employed for assessment of prediction approaches. The performance of the RBFNN protein secondary structure prediction models is evaluated based on their prediction accuracy . The accuracy of the developed approach is compared with other traditional methods to explore the performance of the proposed approach. It is found that the proposed techniques provide a prediction accuracy of about 81% which is very significant. The accuracy for different width of sliding windows. It clearly shows that, with the increase in the sliding window width the accuracy also increases.Keywords
RBFNN, Prediction Accuracy, Training Set, Test Set, Sliding Window
References
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