| Improving prediction of secondary structure, local backbone angles and solvent accessible surface area of proteins by iterative deep learning R Heffernan, K Paliwal, J Lyons, A Dehzangi, A Sharma, J Wang, A Sattar, ... Scientific reports 5 (1), 11476, 2015 | 410 | 2015 |
| Gram-positive and Gram-negative protein subcellular localization by incorporating evolutionary-based descriptors into Chou׳ s general PseAAC A Dehzangi, R Heffernan, A Sharma, J Lyons, K Paliwal, A Sattar Journal of theoretical biology 364, 284-294, 2015 | 258 | 2015 |
| Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto‐encoder deep neural network J Lyons, A Dehzangi, R Heffernan, A Sharma, K Paliwal, A Sattar, Y Zhou, ... Journal of computational chemistry 35 (28), 2040-2046, 2014 | 184 | 2014 |
| Spider2: A package to predict secondary structure, accessible surface area, and main-chain torsional angles by deep neural networks Y Yang, R Heffernan, K Paliwal, J Lyons, A Dehzangi, A Sharma, J Wang, ... Prediction of protein secondary structure, 55-63, 2017 | 173 | 2017 |
| A feature extraction technique using bi-gram probabilities of position specific scoring matrix for protein fold recognition A Sharma, J Lyons, A Dehzangi, KK Paliwal Journal of theoretical biology 320, 41-46, 2013 | 173 | 2013 |
| A review on deep learning approaches in healthcare systems: Taxonomies, challenges, and open issues S Shamshirband, M Fathi, A Dehzangi, AT Chronopoulos, ... Journal of Biomedical Informatics 113, 103627, 2021 | 163 | 2021 |
| Highly accurate sequence-based prediction of half-sphere exposures of amino acid residues in proteins R Heffernan, A Dehzangi, J Lyons, K Paliwal, A Sharma, J Wang, A Sattar, ... Bioinformatics 32 (6), 843-849, 2016 | 104 | 2016 |
| iDTI-ESBoost: identification of drug target interaction using evolutionary and structural features with boosting F Rayhan, S Ahmed, S Shatabda, DM Farid, Z Mousavian, A Dehzangi, ... Scientific reports 7 (1), 17731, 2017 | 100 | 2017 |
| PyFeat: a Python-based effective feature generation tool for DNA, RNA and protein sequences R Muhammod, S Ahmed, D Md Farid, S Shatabda, A Sharma, A Dehzangi Bioinformatics 35 (19), 3831-3833, 2019 | 99 | 2019 |
| iDNAProt-ES: identification of DNA-binding proteins using evolutionary and structural features SY Chowdhury, S Shatabda, A Dehzangi Scientific reports 7 (1), 14938, 2017 | 91 | 2017 |
| Predict gram-positive and gram-negative subcellular localization via incorporating evolutionary information and physicochemical features into Chou's general PseAAC R Sharma, A Dehzangi, J Lyons, K Paliwal, T Tsunoda, A Sharma IEEE transactions on nanobioscience 14 (8), 915-926, 2015 | 88 | 2015 |
| Using rotation forest for protein fold prediction problem: An empirical study A Dehzangi, S Phon-Amnuaisuk, M Manafi, S Safa Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics …, 2010 | 86 | 2010 |
| A tri-gram based feature extraction technique using linear probabilities of position specific scoring matrix for protein fold recognition KK Paliwal, A Sharma, J Lyons, A Dehzangi IEEE transactions on nanobioscience 13 (1), 44-50, 2014 | 81 | 2014 |
| SPIN2: Predicting sequence profiles from protein structures using deep neural networks J O'Connell, Z Li, J Hanson, R Heffernan, J Lyons, K Paliwal, A Dehzangi, ... Proteins: Structure, Function, and Bioinformatics 86 (6), 629-633, 2018 | 78 | 2018 |
| Computer-aided decision-making for predicting liver disease using PSO-based optimized SVM with feature selection JH Joloudari, H Saadatfar, A Dehzangi, S Shamshirband Informatics in medicine unlocked 17, 100255, 2019 | 76 | 2019 |
| PSSM-Suc: Accurately predicting succinylation using position specific scoring matrix into bigram for feature extraction A Dehzangi, Y López, SP Lal, G Taherzadeh, J Michaelson, A Sattar, ... Journal of theoretical biology 425, 97-102, 2017 | 76 | 2017 |
| A combination of feature extraction methods with an ensemble of different classifiers for protein structural class prediction problem A Dehzangi, K Paliwal, A Sharma, O Dehzangi, A Sattar IEEE/ACM Transactions on Computational Biology and Bioinformatics 10 (3 …, 2013 | 65 | 2013 |
| SucStruct: prediction of succinylated lysine residues by using structural properties of amino acids Y López, A Dehzangi, SP Lal, G Taherzadeh, J Michaelson, A Sattar, ... Analytical biochemistry 527, 24-32, 2017 | 60 | 2017 |
| SPRINT-Gly: predicting N- and O-linked glycosylation sites of human and mouse proteins by using sequence and predicted structural properties G Taherzadeh, A Dehzangi, M Golchin, Y Zhou, MP Campbell Bioinformatics 35 (20), 4140-4146, 2019 | 58 | 2019 |
| Improving succinylation prediction accuracy by incorporating the secondary structure via helix, strand and coil, and evolutionary information from profile bigrams A Dehzangi, Y López, SP Lal, G Taherzadeh, A Sattar, T Tsunoda, ... PloS one 13 (2), e0191900, 2018 | 57 | 2018 |
Alok SharmaSenior Scientist at RIKEN; University of the South Pacific; Griffith UniversityVerified email at griffith.edu.au
