Prediction of protein secondary structure using two-layered neural networks

Authors

Timothy Niño S. Travers ⋅ PH National Institute of Molecular Biology and Biotechnology, University of the Philippines Diliman
Christopher P. Monterola ⋅ PH National Institute of Physics, University of the Philippines Diliman
Cynthia P. Palmes-Saloma ⋅ PH National Institute of Molecular Biology and Biotechnology, University of the Philippines Diliman
Caesar A. Saloma ⋅ PH National Institute of Physics, University of the Philippines Diliman

Abstract

Prediction of protein 2° structure can be treated as a problem in pattern recognition for which neural networks can be applied, since certain amino acid sequences favor the formation of certain 2° structures. To determine the optimum parameters for 2° structure prediction by two-layered neural networks, 3-state (helix, sheet, and coil) predictive accuracy was tested depending on the state threshold, the number of inputs used, the number of hidden nodes used, and the number of output nodes used.

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Issue

2002: Proceedings of the 20th Samahang Pisika ng Pilipinas Physics Congress

Ins-FIRE-ing excellence in physics education and research
23-25 October 2002, Ateneo de Naga University

Article ID

SPP-2002-3G-02

Section

Computational Physics

Published

2002-10-23

How to Cite

[1]

TNS Travers, CP Monterola, CP Palmes-Saloma, and CA Saloma, Prediction of protein secondary structure using two-layered neural networks, Proceedings of the Samahang Pisika ng Pilipinas 20, SPP-2002-3G-02 (2002). URL: https://proceedings.spp-online.org/article/view/SPP-2002-3G-02.