Citation
  1. Shi-Hao Feng, Wei-Xun Zhang, Jing Yang, Yang Yang, and Hong-Bin Shen, Topology prediction improvement of α-helical transmembrane proteins through helix-tail modeling and multi-scale deep learning fusion, Submitted, 2019.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. Hongbin Shen, James J. Chou, MemBrain: Improving the Accuracy of Predicting Transmembrane Helices, PLoS ONE, 2008, 6: e2399.
  7. 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.