Prediction rule generation of MHC class i binding peptides using ANN and GA

Authors:
Yeon-Jin Cho;Hyeoncheol Kim;Heung-Bum Oh
Affiliations:
Dept. of Computer Science Education, Korea University, Seoul, Korea;Dept. of Computer Science Education, Korea University, Seoul, Korea;Dept. of Laboratory Medicine, University of Ulsan and Asan Medical Center, Seoul, Korea
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Year:
2005

Citing 7
Cited 0

Genetic algorithms + data structures = evolution programs (3rd ed.)

Genetic algorithms + data structures = evolution programs (3rd ed.)
An introduction to genetic algorithms

An introduction to genetic algorithms
An introduction to support Vector Machines: and other kernel-based learning methods

An introduction to support Vector Machines: and other kernel-based learning methods
Bioinformatics: the machine learning approach

Bioinformatics: the machine learning approach
Computationally Efficient Heuristics for If-Then Rule Extraction from Freed-Forward Neural Networks

DS '00 Proceedings of the Third International Conference on Discovery Science
Hybrid learning using genetic algorithms and decision trees for pattern classification

IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Learning concept classification rules using genetic algorithms

IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2

Quantified Score

Hi-index	0.00

Visualization

Abstract

A new method is proposed for generating if-then rules to predict peptide binding to class I MHC proteins, from the amino acid sequence of any protein with known binders and non-binders. In this paper, we present an approach based on artificial neural networks (ANN) and knowledge-based genetic algorithm (KBGA) to predict the binding of peptides to MHC class I molecules. Our method includes rule extraction from a trained neural network and then enhancing the extracted rules by genetic evolution. Experimental results show that the method could generate new rules for MHC class I binding peptides prediction.