Extreme learning machine for predicting HLA-Peptide binding

Authors:
Stephanus Daniel Handoko;Kwoh Chee Keong;Ong Yew Soon;Guang Lan Zhang;Vladimir Brusic
Affiliations:
School of Computer Engineering, Nanyang Technological University, Singapore;School of Computer Engineering, Nanyang Technological University, Singapore;School of Computer Engineering, Nanyang Technological University, Singapore;School of Computer Engineering, Nanyang Technological University, Singapore;School of Land and Food Sciences and Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Year:
2006

Citing 2
Cited 3

Introduction to Artificial Neural Systems

Introduction to Artificial Neural Systems
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)

A study on metamodeling techniques, ensembles, and multi-surrogates in evolutionary computation

Proceedings of the 9th annual conference on Genetic and evolutionary computation
A fast pruned-extreme learning machine for classification problem

Neurocomputing
Aircraft recognition using modular extreme learning machine

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.