Saras Saraswathi, Robert L. Jernigan, Andrzej Kloczkowski, Andrzej Kolinski


A novel method is proposed for predicting protein secondary structure using data derived from knowledge based potentials and Neural Networks. Potential energies for amino acid sequences in proteins are calculated using protein structures. An Extreme Learning Machine classifier (ELM-PSO) is used to model and predict protein secondary structures. Classifier performance is maximized using the Particle Swarm Optimization algorithm. Preliminary results show improved results.


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Paper Citation

in Harvard Style

Saraswathi S., Jernigan R., Kloczkowski A. and Kolinski A. (2010). PROTEIN SECONDARY STRUCTURE PREDICTION USING KNOWLEDGE-BASED POTENTIALS . 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 370-375. DOI: 10.5220/0003086903700375

in Bibtex Style

author={Saras Saraswathi and Robert L. Jernigan and Andrzej Kloczkowski and Andrzej Kolinski},
booktitle={Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICNC, (IJCCI 2010)},

in EndNote Style

JO - Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICNC, (IJCCI 2010)
SN - 978-989-8425-32-4
AU - Saraswathi S.
AU - Jernigan R.
AU - Kloczkowski A.
AU - Kolinski A.
PY - 2010
SP - 370
EP - 375
DO - 10.5220/0003086903700375