Found: 8
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Gram-positive and gram-negative subcellular localization using rotation forest and physicochemical-based features.
- Published in:
- BMC Bioinformatics, 2015, v. 16, p. 1, doi. 10.1186/1471-2105-16-S4-S1
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- Article
Sixty-five years of the long march in protein secondary structure prediction: the final stretch?
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- Briefings in Bioinformatics, 2018, v. 19, n. 3, p. 482, doi. 10.1093/bib/bbw129
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- Article
Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning.
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- Scientific Reports, 2015, p. 11476, doi. 10.1038/srep11476
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- Article
SPIN2: Predicting sequence profiles from protein structures using deep neural networks.
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- Proteins, 2018, v. 86, n. 6, p. 629, doi. 10.1002/prot.25489
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- Article
Capturing non-local interactions by long short-term memory bidirectional recurrent neural networks for improving prediction of protein secondary structure, backbone angles, contact numbers and solvent accessibility.
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- Bioinformatics, 2017, v. 33, n. 18, p. 2842, doi. 10.1093/bioinformatics/btx218
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- Article
Highly accurate sequence-based prediction of half-sphere exposures of amino acid residues in proteins.
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- Bioinformatics, 2016, v. 32, n. 6, p. 843, doi. 10.1093/bioinformatics/btv665
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- Article
Single‐sequence‐based prediction of protein secondary structures and solvent accessibility by deep whole‐sequence learning.
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- Journal of Computational Chemistry, 2018, v. 39, n. 26, p. 2210, doi. 10.1002/jcc.25534
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- Article
Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto-encoder deep neural network.
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- Journal of Computational Chemistry, 2014, v. 35, n. 28, p. 2040, doi. 10.1002/jcc.23718
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- Article