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 |
|---|---|
| Title of host publication | Lecture Notes in Computer Science |
| Editors | L. Wang, K. Chen, Y.S. Ong |
| Pages | 1009-1016 |
| Number of pages | 8 |
| Volume | 3610 |
| Edition | PART I |
| Publication status | Published - 2005 |
| Event | First International Conference on Natural Computation, ICNC 2005 - Changsha, China Duration: 2005 Aug 27 → 2005 Aug 29 |
Other
| Other | First International Conference on Natural Computation, ICNC 2005 |
|---|---|
| Country | China |
| City | Changsha |
| Period | 05/8/27 → 05/8/29 |
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ASJC Scopus subject areas
- Computer Science (miscellaneous)
Cite this
Prediction rule generation of MHC class I binding peptides using ANN and GA. / Cho, Yeon J.; Kim, Hyeoncheol; Oh, Heung B.
Lecture Notes in Computer Science. ed. / L. Wang; K. Chen; Y.S. Ong. Vol. 3610 PART I. ed. 2005. p. 1009-1016.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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TY - GEN
T1 - Prediction rule generation of MHC class I binding peptides using ANN and GA
AU - Cho, Yeon J.
AU - Kim, Hyeoncheol
AU - Oh, Heung B.
PY - 2005
Y1 - 2005
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.
UR - http://www.scopus.com/inward/record.url?scp=26844482649&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=26844482649&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:26844482649
VL - 3610
SP - 1009
EP - 1016
BT - Lecture Notes in Computer Science
A2 - Wang, L.
A2 - Chen, K.
A2 - Ong, Y.S.
ER -