AN EXTREME LEARNING MACHINE CLASSIFIER FOR PREDICTION OF RELATIVE SOLVENT ACCESSIBILITY IN PROTEINS

Saras Saraswathi, Robert L. Jernigan, Andrzej Kloczkowski

2010

Abstract

A neural network based method called Sparse-Extreme Learning Machine (S-ELM) is used for prediction of Relative Solvent Accessibility (RSA) in proteins. We have shown that multiple-fold gains in speed of processing by S-ELM compared to using SVM for classification, while accuracy efficiencies are comparable to literature. The study indicates that using S-ELM would give a distinct advantage in terms of processing speed and performance for RSA prediction.

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Paper Citation


in Harvard Style

Saraswathi S., Jernigan R. and Kloczkowski A. (2010). AN EXTREME LEARNING MACHINE CLASSIFIER FOR PREDICTION OF RELATIVE SOLVENT ACCESSIBILITY IN PROTEINS . In Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICNC, (IJCCI 2010) ISBN 978-989-8425-32-4, pages 364-369. DOI: 10.5220/0003086803640369


in Bibtex Style

@conference{icnc10,
author={Saras Saraswathi and Robert L. Jernigan and Andrzej Kloczkowski},
title={AN EXTREME LEARNING MACHINE CLASSIFIER FOR PREDICTION OF RELATIVE SOLVENT ACCESSIBILITY IN PROTEINS },
booktitle={Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICNC, (IJCCI 2010)},
year={2010},
pages={364-369},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003086803640369},
isbn={978-989-8425-32-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICNC, (IJCCI 2010)
TI - AN EXTREME LEARNING MACHINE CLASSIFIER FOR PREDICTION OF RELATIVE SOLVENT ACCESSIBILITY IN PROTEINS
SN - 978-989-8425-32-4
AU - Saraswathi S.
AU - Jernigan R.
AU - Kloczkowski A.
PY - 2010
SP - 364
EP - 369
DO - 10.5220/0003086803640369