A system that resorts to multiple experts for dealing with the problem of predicting secondary structures is described, whose performances are comparable to those obtained by other state-of-the-art predictors. The system performs an overall processing based on two main steps: first, a "sequence-to-structure" prediction is performed, by resorting to a population of hybrid genetic-neural experts, and then a "structure-to-structure" prediction is performed, by resorting to a feedforward artificial neural networks. To investigate the performance of the proposed approach, the system has been tested on the RS126 set of proteins. Experimental results (about 76% of accuracy) point to the validity of the approach.
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Armano, G., Orro, A. & Vargiu, E. MASSP3: A System for Predicting Protein Secondary Structure. EURASIP J. Adv. Signal Process. 2006, 017195 (2006). https://doi.org/10.1155/ASP/2006/17195
- Neural Network
- Information Technology
- Secondary Structure
- Artificial Neural Network
- Quantum Information