Extreme learning machine for predicting HLA-peplide binding

Stephanus Daniel Handoko, Kwoh Chee Keong, Ong Yew Soon, Guang Lan Zhang, Vladimir Brusic

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationAdvances in Neural Networks - ISNN 2006
Subtitle of host publicationThird International Symposium on Neural Networks, ISNN 2006, Proceedings - Part III
PublisherSpringer Verlag
Pages716-721
Number of pages6
ISBN (Print)3540344829, 9783540344827
DOIs
Publication statusPublished - Jan 1 2006
Event3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks - Chengdu, China
Duration: May 28 2006Jun 1 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3973 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks
CountryChina
CityChengdu
Period5/28/066/1/06

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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