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Authors: Saras Saraswathi ; Robert L. Jernigan and Andrzej Kloczkowski

Affiliation: L. H. Baker Center for Bioinformatics and Biological Statistics and Iowa State University, United States

Keyword(s): Relative solvent accessibility, Support vector machine, Neural network, Extreme learning machine, Prediction.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Health Engineering and Technology Applications ; Higher Level Artificial Neural Network Based Intelligent Systems ; Human-Computer Interaction ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neuroinformatics and Bioinformatics ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Supervised and Unsupervised Learning ; Support Vector Machines and Applications ; Theory and Methods

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 several formats:
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 (IJCCI 2010) - ICNC; ISBN 978-989-8425-32-4, SciTePress, pages 364-369. DOI: 10.5220/0003086803640369

@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 (IJCCI 2010) - ICNC},
year={2010},
pages={364-369},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003086803640369},
isbn={978-989-8425-32-4},
}

TY - CONF

JO - Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation (IJCCI 2010) - ICNC
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
PB - SciTePress

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