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Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto-encoder deep neural network.

, , , , , , , and . J. Comput. Chem., 35 (28): 2040-2046 (2014)

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Detecting Proline and Non-Proline Cis Isomers in Protein Structures from Sequences Using Deep Residual Ensemble Learning., , , , , and . J. Chem. Inf. Model., 58 (9): 2033-2042 (2018)Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto-encoder deep neural network., , , , , , , and . J. Comput. Chem., 35 (28): 2040-2046 (2014)Highly accurate sequence-based prediction of half-sphere exposures of amino acid residues in proteins., , , , , , , , and . Bioinform., 32 (6): 843-849 (2016)Gram-positive and gram-negative subcellular localization using rotation forest and physicochemical-based features., , , , , , and . BMC Bioinform., 16 (S-4): S1 (2015)Single-sequence-based prediction of protein secondary structures and solvent accessibility by deep whole-sequence learning., , , , , and . J. Comput. Chem., 39 (26): 2210-2216 (2018)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., , , and . Bioinform., 33 (18): 2842-2849 (2017)Sixty-five years of the long march in protein secondary structure prediction: the final stretch?, , , , , , and . Briefings Bioinform., 19 (3): 482-494 (2018)