Chen, Z., Zhao, P., Li, F., Marquez-Lago, T.T., Leier, A., Revote, J., Zhu, Y., Powell, D.R., Akutsu, T., Webb, G.I., Chou, K.C., Smith, A.I., Daly, R.J., Li, J. and Song, J. (2019) iLearn: An Integrated Platform and Meta-Learner for Eature Engineering, Machine-Learning Analysis and Modeling of DNA, RNA and Protein Sequence Data, Brief. Bioinform.
https://doi.org/10.1093/bib/bbz041

has been cited by the following article:

• TITLE: An Insightful Recollection for Predicting Protein Subcellular Locations in Multi-Label Systems

  AUTHORS: Kuo-Chen Chou

  KEYWORDS: Chou’s 5-Steps Rule, Chou’s PseAAC, Web-Server GO Approach, FunD Approach

  JOURNAL NAME: Natural Science, Vol.12 No.7, July 15, 2020

  ABSTRACT: A systematic introduction has been presented for the recent advances in predicting protein subcellular localization in the multi-label systems, where the constituent proteins may simultaneously occur or move between two or more location sites and hence have exceptional biological functions worthy of our special notice. All the predictors included in this review each have a user-friendly web-server, by which the majority of experimental scientists can very easily acquire their desired data without the need to go through the complicated mathematics involved.