NDLI: Protein Fold Pattern Recognition Using Bayesian Ensemble of RBF Neural Networks

Content Provider	IEEE Xplore Digital Library
Author	Hashemi, H.B. Shakery, A. Naeini, M.P.
Copyright Year	2009
Abstract	Protein fold pattern recognition has been one of the most challenging problems in biology during the last 40 years. Recently due to the vast improvement in machine learning and pattern recognition methods many computer scientists have applied these methods to solve this problem. However, protein folding problem is much more complicated than ordinary machine learning problems because of its natural complexity imposed by the high dimensionality of feature space and diversity of different protein fold classes. To deal with such a challenging problem, we use an ensemble classifier model by applying MLP and RBF Neural Networks and Bayesian ensemble method. Also we have used the Laplace estimation method in order to smooth confusion matrices of the base classifiers. Experimental results imply that RBF Neural Network holds better Correct Classification Rate (CCR) compared to other common classification methods such as MLP networks. Our experiments also show that the Bayesian fusion method can improve the correct classification rate of proteins up to 20% with the final CCR of 59% by reducing both bias and variance error of the RBF classifiers, on a benchmark dataset containing 27 SCOP folds.
Starting Page	436
Ending Page	441
File Size	457758
Page Count	6
File Format	PDF
ISBN	9781424453306
DOI	10.1109/SoCPaR.2009.91
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-04
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neurons Artificial neural networks Amino acids Bayesian Classifier Fusion Pattern recognition Protein Folding Proteins MLP Bayesian methods Neural networks Machine learning RBF Biology computing Computer networks
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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