Accurate prediction of protein contact maps by coupling residual two-dimensional bidirectional long short-term memory with convolutional neural networks
Jack Hanson(Griffith University), Yaoqi Zhou(China University of Geosciences (Beijing)), Yuedong Yang(Guangzhou Experimental Station), Kuldip K. Paliwal(Griffith University), Thomas Litfin(Griffith University)
Cited by 203
Related Papers
Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images
|IEEE/ACM Transactions on Computational Biology and Bioinformatics|2021|836
Real-time reliable determination of binding kinetics of DNA hybridization using a multi-channel graphene biosensor
|Nature Communications|2017|430
Capturing non-local interactions by long short-term memory bidirectional recurrent neural networks for improving prediction of protein secondary structure, backbone angles, contact numbers and solvent accessibility
|Bioinformatics|2017|389
Improving prediction of secondary structure, local backbone angles and solvent accessible surface area of proteins by iterative deep learning
|Scientific Reports|2015|365
Critical assessment of protein intrinsic disorder prediction
|Nature Methods|2021|362