|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
|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
|Capturing Non-Local Interactions by Long Short Term Memory Bidirectional Recurrent Neural Networks for Improving Prediction of Protein Secondary Structure, Backbone Angles …|
R Heffernan, Y Yang, K Paliwal, Y Zhou
|Sixty-five years of the long march in protein secondary structure prediction: the final stretch?|
Y Yang, J Gao, J Wang, R Heffernan, J Hanson, K Paliwal, Y Zhou
Briefings in bioinformatics 19 (3), 482-494, 2018
|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
|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
|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
|Gram-positive and gram-negative subcellular localization using rotation forest and physicochemical-based features|
A Dehzangi, S Sohrabi, R Heffernan, A Sharma, J Lyons, K Paliwal, ...
BMC bioinformatics 16 (S4), S1, 2015
|Advancing the accuracy of protein fold recognition by utilizing profiles from hidden Markov models|
J Lyons, A Dehzangi, R Heffernan, Y Yang, Y Zhou, A Sharma, K Paliwal
IEEE transactions on nanobioscience 14 (7), 761-772, 2015
|Single‐sequence‐based prediction of protein secondary structures and solvent accessibility by deep whole‐sequence learning|
R Heffernan, K Paliwal, J Lyons, J Singh, Y Yang, Y Zhou
Journal of computational chemistry 39 (26), 2210-2216, 2018
|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
|Protein fold recognition using HMM–HMM alignment and dynamic programming|
J Lyons, KK Paliwal, A Dehzangi, R Heffernan, T Tsunoda, A Sharma
Journal of Theoretical Biology 393, 67-74, 2016
|A short review of deep learning neural networks in protein structure prediction problems|
K Paliwal, J Lyons, R Heffernan
Advanced Techniques in Biology & Medicine, 1-2, 2015
|Detecting proline and non-proline cis isomers in protein structures from sequences using deep residual ensemble learning|
J Singh, J Hanson, R Heffernan, K Paliwal, Y Yang, Y Zhou
Journal of chemical information and modeling 58 (9), 2033-2042, 2018
|Addressing One-Dimensional Protein Structure Prediction Problems with Machine Learning Techniques|
Griffith University, Brisbane, Australia, 2018