Prediction of protein secondary structure at high accuracy using a combination of many neural networks

被引：0

作者：

Lundegaard, Claus ^{[1
]}

Petersen, Thomas Nordahl ^{[1
]}

Nielsen, Morten ^{[1
]}

Bohr, Henrik ^{[2
]}

Bohr, Jacob ^{[2
]}

Brunak, Søren ^{[2
]}

Gippert, Carry ^{[1
]}

Lund, Ole ^{[1
]}

机构：

[1] Structural Bioinformatics Advanced Technologies A/S, Agern Allé 3, DK-2970 Hørsholm, Denmark

[2] Structural Bioinformatics Inc., SAB, San Diego, CA, United States

来源：

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | 2003年 / 2666卷

关键词：

Proteins; -; Forecasting;

D O I：

10.1007/978-3-540-44827-3_7

中图分类号：

学科分类号：

摘要：

A protein secondary structure prediction protocol involving up to 800 neural network predictions has been developed by SBI-AT. An overall performance of 80% is obtained for three-state (helix, strand, coil) DSSP categories. Input to primary-layer neural networks includes sequence profiles, relative residue position, relative chain length, and amino-acid composition. Secondary structure predictions are made for three consecutive residues simultaneously - a technique which we describe as 'output expansion' - which boosts the performance of second-layer structure-to-structure networks. Independent network predictions arise from 10-fold cross validated training and testing of 1032 protein sequences at both primary and secondary network layers. Network output activities are converted to probabilities. Finally, 800 different predictions are combined using a novel balloting procedure. © Springer-Verlag Berlin Heidelberg 2003.

引用

页码：117 / 122

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