Rhys Heffernan
Rhys Heffernan
Verified email at griffith.edu.au
Cited by
Cited by
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
Bioinformatics, 2017
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
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
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
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 (4), 1-8, 2015
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
R Heffernan
Griffith University, Brisbane, Australia, 2018
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