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Erschienen in:
2006 | OriginalPaper | Buchkapitel
Extreme Learning Machine for Predicting HLA-Peptide Binding
verfasst von : Stephanus Daniel Handoko, Kwoh Chee Keong, Ong Yew Soon, Guang Lan Zhang, Vladimir Brusic
Erschienen in: Advances in Neural Networks - ISNN 2006
Verlag: Springer Berlin Heidelberg
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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.