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

Yeon Jin Cho; Hyeoncheol Kim; Heung Bum Oh

doi:10.1007/11539087_133

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

Yeon Jin Cho, Hyeoncheol Kim, Heung Bum Oh

Research output: Contribution to journal › Conference article › peer-review

2 Citations (Scopus)

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.

Original language	English
Pages (from-to)	1009-1016
Number of pages	8
Journal	Lecture Notes in Computer Science
Volume	3610
Issue number	PART I
DOIs	https://doi.org/10.1007/11539087_133
Publication status	Published - 2005
Event	First International Conference on Natural Computation, ICNC 2005 - Changsha, China Duration: 2005 Aug 27 → 2005 Aug 29

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

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10.1007/11539087_133

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title = "Prediction rule generation of MHC class I binding peptides using ANN and GA",

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.",

author = "Cho, {Yeon Jin} and Hyeoncheol Kim and Oh, {Heung Bum}",

year = "2005",

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AU - Cho, Yeon Jin

AU - Kim, Hyeoncheol

AU - Oh, Heung Bum

PY - 2005

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N2 - 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.

AB - 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.

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JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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