Citation
  1. Feng, Shi-Hao, et al. Topology prediction improvement of α-helical transmembrane proteins through helix-tail modeling and multiscale deep learning fusion. Journal of molecular biology 432.4 (2020): 1279-1296.
  2. Feng, Shi-Hao, et al. Ab-initio Membrane Protein Amphipathic Helix Structure Prediction Using Deep Neural Networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics (2020).
  3. Jing Yang and Hong-Bin Shen, MemBrain-contact 2.0: A new two-stage deep learning model for the prediction enhancement of transmembrane protein residue contacts in the full chain, Bioinformatics, 2018, 34: 230-238.
  4. Xi Yin, Jing Yang, Feng Xiao, Yang Yang, and Hong-Bin Shen, MemBrain: An easy-to-use online webserver for transmembrane protein structure prediction, Nano-Micro Lett, 2018, 10: 2. http://dx.doi.org/10.1007/s40820-017-0156-2.
  5. Feng Xiao and Hong-Bin Shen, Prediction enhancement of residue real-value relative accessible surface area in transmembrane helical proteins by solving the output preference problem of machine learning-based predictors, Journal of Chemical Information and Modeling, 2015, 55: 2464-2474.
  6. Jing Yang, Richard Jang, Yang Zhang, and Hong-Bin Shen, High-accuracy prediction of transmembrane inter-helix contacts and application to GPCR 3D structure modeling, Bioinformatics, 2013, 29: 2579-2587.
  7. Hongbin Shen, James J. Chou, MemBrain: Improving the Accuracy of Predicting Transmembrane Helices, PLoS ONE, 2008, 6: e2399.
  8. Hong-Bin Shen, Kuo-Chen Chou, Signal-3L: a 3-layer approach for predicting signal peptides, Biochemical and Biophysical Research Communications, 2007, 363: 297-303.