TY - GEN
T1 - Extreme learning machine for predicting HLA-peplide binding
AU - Handoko, Stephanus Daniel
AU - Keong, Kwoh Chee
AU - Soon, Ong Yew
AU - Zhang, Guang Lan
AU - Brusic, Vladimir
N1 - Copyright:
Copyright 2015 Elsevier B.V., All rights reserved.
PY - 2006
Y1 - 2006
N2 - Machine learning techniques have been recognized as powerful tools for learning from data. One of the most popular learning techniques, the Back-Propagation (BP) Artificial Neural Networks, can be used as a computer model to predict peptides binding to the Human Leukocyte Antigens (HLA). The major advantage of computational screening is that it reduces the number of wet-lab experiments that need to be performed, significantly reducing the cost and time. A recently developed method, Extreme Learning Machine (ELM), which has superior properties over BP has been investigated to accomplish such tasks. In our work, we found that the ELM is as good as, if not better than, the BP in term of time complexity, accuracy deviations across experiments, and - most importantly - prevention from over-fitting for prediction of peptide binding to HLA.
AB - Machine learning techniques have been recognized as powerful tools for learning from data. One of the most popular learning techniques, the Back-Propagation (BP) Artificial Neural Networks, can be used as a computer model to predict peptides binding to the Human Leukocyte Antigens (HLA). The major advantage of computational screening is that it reduces the number of wet-lab experiments that need to be performed, significantly reducing the cost and time. A recently developed method, Extreme Learning Machine (ELM), which has superior properties over BP has been investigated to accomplish such tasks. In our work, we found that the ELM is as good as, if not better than, the BP in term of time complexity, accuracy deviations across experiments, and - most importantly - prevention from over-fitting for prediction of peptide binding to HLA.
UR - http://www.scopus.com/inward/record.url?scp=33745905909&partnerID=8YFLogxK
U2 - 10.1007/11760191_105
DO - 10.1007/11760191_105
M3 - Conference contribution
AN - SCOPUS:33745905909
SN - 3540344829
SN - 9783540344827
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 716
EP - 721
BT - Advances in Neural Networks - ISNN 2006
PB - Springer Verlag
T2 - 3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks
Y2 - 28 May 2006 through 1 June 2006
ER -