A simple and fast secondary structure prediction method using hidden neural networks | Zendy

Kuang Lin | Zendy; V. A. Simossis | Zendy; Willam R. Taylor | Zendy; Jaap Heringa | Zendy

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A simple and fast secondary structure prediction method using hidden neural networks

Author(s) -

Kuang Lin,

V. A. Simossis,

Willam R. Taylor,

Jaap Heringa

Publication year - 2004

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bth487

Subject(s) - artificial neural network , set (abstract data type) , computer science , simple (philosophy) , hidden markov model , artificial intelligence , sequence (biology) , algorithm , data mining , machine learning , pattern recognition (psychology) , biology , philosophy , epistemology , genetics , programming language

In this paper, we present a secondary structure prediction method YASPIN that unlike the current state-of-the-art methods utilizes a single neural network for predicting the secondary structure elements in a 7-state local structure scheme and then optimizes the output using a hidden Markov model, which results in providing more information for the prediction.

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