| 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), 1-11, 2015 | 226 | 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 | 191 | 2015 |
| 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 | 123 | 2013 |
| 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 | 107 | 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 | 75 | 2017 |
| 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 | 61 | 2016 |
| 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 | 60 | 2015 |
| 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 | 59 | 2014 |
| 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 | 51 | 2013 |
| Using Random Forest for Protein Fold Prediction Problem: An Empirical Study. A Dehzangi, S Phon-Amnuaisuk, O Dehzangi J. Inf. Sci. Eng. 26 (6), 1941-1956, 2010 | 49 | 2010 |
| A strategy to select suitable physicochemical attributes of amino acids for protein fold recognition A Sharma, KK Paliwal, A Dehzangi, J Lyons, S Imoto, S Miyano BMC bioinformatics 14 (1), 233, 2013 | 46 | 2013 |
| Proposing a highly accurate protein structural class predictor using segmentation-based features A Dehzangi, K Paliwal, J Lyons, A Sharma, A Sattar BMC genomics 15 (S1), S2, 2014 | 41 | 2014 |
| SucStruct: prediction of succinylated lysine residues by using structural properties of amino acids Y Lopez, A Dehzangi, SP Lal, G Taherzadeh, J Michaelson, A Sattar, ... Analytical biochemistry 527, 24-32, 2017 | 37 | 2017 |
| 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), 1-18, 2017 | 33 | 2017 |
| A Segmentation-based Method to Extract Structural and Evolutionary Features for Protein Fold Recognition A Dehzangi, J Lyons, A Sharma, A Sattar IEEE/ACM Transactions on Computational Biology and Bioinformatics 11 (3 …, 2014 | 33 | 2014 |
| 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 | 32 | 2017 |
| Protein fold recognition by alignment of amino acid residues using kernelized dynamic time warping J Lyons, N Biswas, A Sharma, A Dehzangi, KK Paliwal Journal of theoretical biology 354, 137-145, 2014 | 32 | 2014 |
| Fold prediction problem: The application of new physical and physicochemical-based features A Dehzangi, S Phon-Amnuaisuk Protein and Peptide Letters 18 (2), 174-185, 2011 | 30 | 2011 |
| Using rotation forest for protein fold prediction problem: An empirical study A Dehzangi, S Phon-Amnuaisuk, M Manafi, S Safa European Conference on Evolutionary Computation, Machine Learning and Data …, 2010 | 29 | 2010 |
| iDNAProt-ES: Identification of dna-binding proteins using evolutionary and structural features SY Chowdhury, S Shatabda, A Dehzangi Scientific reports 7 (1), 1-14, 2017 | 28 | 2017 |
Alok SharmaSenior Scientist at RIKEN; University of the South Pacific; Griffith UniversityVerified email at griffith.edu.au
