| 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 | 436 | 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 | 356 | 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 | 284 | 2018 |
| 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 | 267 | 2015 |
| 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 | 189 | 2017 |
| 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 | 188 | 2014 |
| 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 | 111 | 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 | 108 | 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 | 97 | 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 | 42 | 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 | 40 | 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, 1-8, 2015 | 38 | 2015 |
| A short review of deep learning neural networks in protein structure prediction problems K Paliwal, J Lyons, R Heffernan Adv. Tech. Biol. Med 3 (3), 2379-1764.1000139, 2015 | 24 | 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 | 17 | 2018 |
| Developing deep learning applications for life science and pharma industry D Siegismund, V Tolkachev, S Heyse, B Sick, O Duerr, S Steigele Drug research 68 (06), 305-310, 2018 | 13 | 2018 |
Kuldip PaliwalProfessor (Chair, Communication and Information Engineering), Griffith University, BrisbaneVerified email at griffith.edu.au
