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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Vydané v:	BMC bioinformatics Ročník 18; číslo Suppl 16; s. 569 - 220
Hlavní autori:	Deng, Lei, Fan, Chao, Zeng, Zhiwen
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London BioMed Central 28.12.2017 BioMed Central Ltd BMC
Predmet:	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
On-line prístup:	Získať plný text
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