A Hybrid Genetic-Neural System for Predicting Protein Secondary Structure
| Autor: | Massimiliano Saba, Eloisa Vargiu, Gianmaria Mancosu, Giuliano Armano, Alessandro Orro, Luciano Milanesi |
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| Jazyk: | angličtina |
| Předmět: |
Models
Molecular Protein Folding Relation (database) Protein Conformation Bioinformatics Molecular Sequence Data 02 engineering and technology Computational biology Biology Machine learning computer.software_genre Proteomics Biochemistry Protein Structure Secondary Task (project management) 03 medical and health sciences Protein structure Software Sequence Analysis Protein Structural Biology 0202 electrical engineering electronic engineering information engineering Genetics Computer Simulation Databases Protein Protein secondary structure Molecular Biology 030304 developmental biology Neural Structure (mathematical logic) 0303 health sciences symulation business.industry Applied Mathematics Protein Computational Biology Proteins Protein structure prediction Protein Structure Tertiary Computer Science Applications Models Chemical 020201 artificial intelligence & image processing Neural Networks Computer Artificial intelligence business Sequence Alignment computer Algorithms Research Article |
| Zdroj: | BMC bioinformatics 6 (2005). doi:10.1186/1471-2105-6-S4-S3 info:cnr-pdr/source/autori:Armano, G; Mancosu, G; Milanesi, L; Orro, A; Saba, M; Vargiu, E/titolo:A hybrid genetic-neural system for predicting protein secondary structure/doi:10.1186%2F1471-2105-6-S4-S3/rivista:BMC bioinformatics/anno:2005/pagina_da:/pagina_a:/intervallo_pagine:/volume:6 BMC Bioinformatics |
| ISSN: | 1471-2105 |
| DOI: | 10.1186/1471-2105-6-s4-s3 |
| Popis: | Background Due to the strict relation between protein function and structure, the prediction of protein 3D-structure has become one of the most important tasks in bioinformatics and proteomics. In fact, notwithstanding the increase of experimental data on protein structures available in public databases, the gap between known sequences and known tertiary structures is constantly increasing. The need for automatic methods has brought the development of several prediction and modelling tools, but a general methodology able to solve the problem has not yet been devised, and most methodologies concentrate on the simplified task of predicting secondary structure. Results In this paper we concentrate on the problem of predicting secondary structures by adopting a technology based on multiple experts. The system performs an overall processing based on two main steps: first, a "sequence-to-structure" prediction is enforced by resorting to a population of hybrid (genetic-neural) experts, and then a "structure-to-structure" prediction is performed by resorting to an artificial neural network. Experiments, performed on sequences taken from well-known protein databases, allowed to reach an accuracy of about 76%, which is comparable to those obtained by state-of-the-art predictors. Conclusion The adoption of a hybrid technique, which encompasses genetic and neural technologies, has demonstrated to be a promising approach in the task of protein secondary structure prediction. |
| Databáze: | OpenAIRE |
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