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Improving protein fold recognition using the amalgamation of evolutionary-based and structural based information.

, , , and . BMC Bioinformatics, 15 (S-16): S12 (2014)

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Gram-positive and gram-negative subcellular localization using rotation forest and physicochemical-based features., , , , , , and . BMC Bioinformatics, 16 (S-4): S1 (2015)A strategy to select suitable physicochemical attributes of amino acids for protein fold recognition., , , , , and . BMC Bioinformatics, (2013)Protein Fold Recognition Using an Overlapping Segmentation Approach and a Mixture of Feature Extraction Models., , , , and . Australasian Conference on Artificial Intelligence, volume 8272 of Lecture Notes in Computer Science, page 32-43. Springer, (2013)Protein Fold Recognition Using Segmentation-Based Feature Extraction Model., and . ACIIDS (1), volume 7802 of Lecture Notes in Computer Science, page 345-354. Springer, (2013)SPRINT-Gly: predicting N- and O-linked glycosylation sites of human and mouse proteins by using sequence and predicted structural properties., , , , and . Bioinformatics, 35 (20): 4140-4146 (2019)Enhancing Protein Fold Prediction Accuracy Using an Ensemble of Different Classifiers., , and . Austr. J. Intelligent Information Processing Systems, (2009)Protein Fold Recognition Using Genetic Algorithm Optimized Voting Scheme and Profile Bigram., , , , , and . JSW, 11 (8): 756-767 (2016)Highly accurate sequence-based prediction of half-sphere exposures of amino acid residues in proteins., , , , , , , , and . Bioinformatics, 32 (6): 843-849 (2016)Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto-encoder deep neural network., , , , , , , and . Journal of Computational Chemistry, 35 (28): 2040-2046 (2014)GlyStruct: glycation prediction using structural properties of amino acid residues., , , , , and . BMC Bioinformatics, 19-S (13): 55-64 (2019)