A sparse autoencoder-based deep neural network for protein solvent accessibility and contact number prediction

Background Direct prediction of the three-dimensional (3D) structures of proteins from one-dimensional (1D) sequences is a challenging problem. Significant structural characteristics such as solvent accessibility and contact number are essential for deriving restrains in modeling protein folding and...

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Veröffentlicht in:	BMC bioinformatics Jg. 18; H. Suppl 16; S. 569 - 220
Hauptverfasser:	Deng, Lei, Fan, Chao, Zeng, Zhiwen
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 28.12.2017 BioMed Central Ltd BMC
Schlagworte:	Algorithms Amino acids Analysis Artificial neural networks Bioinformatics Biomedical and Life Sciences Computational Biology/Bioinformatics Computer Appl. in Life Sciences Contact number Deep neural network Life Sciences Machine Learning Microarrays Models, Molecular Neural Networks, Computer Proteins Proteins - chemistry Sequence-derived features Solvent accessibility Solvents - chemistry Deep neural network Sequence-derived features Contact number Solvent accessibility
ISSN:	1471-2105, 1471-2105
Online-Zugang:	Volltext
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