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Zhang, M., Li, F., Marquez-Lago, T.T., Leier, A., Fan, C., Kwoh, C.K., Chou, K.C., Song, J. and Jia, C. (2019) MULTiPly: A Novel Multi-Layer Predictor for Discovering General and Specific Types of Promoters. Bioinformatics, 35, 2957-2965.
https://doi.org/10.1093/bioinformatics/btz016

has been cited by the following article:

• TITLE: pLoc_Deep-mAnimal: A Novel Deep CNN-BLSTM Network to Predict Subcellular Localization of Animal Proteins

  AUTHORS: Yu-Tao Shao, Kuo-Chen Chou

  KEYWORDS: Pandemic Coronavirus, Multi-Label System, Animal Proteins, Learning at Deeper Level, Five Steps Rule, PseAAC

  JOURNAL NAME: Natural Science, Vol.12 No.5, May 14, 2020

  ABSTRACT: Current coronavirus pandemic has endangered mankind life. The reported cases are increasing exponentially. Information of animal protein subcellular localization can provide useful clues to develop antiviral drugs. To cope with such a catastrophe, a CNN based animal protein subcellular localization predictor called “pLoc_Deep-mAnimal” was developed. The predictor is particularly useful in dealing with the multi-sites systems in which some proteins may simultaneously occur in two or more different organelles that are the current focus of pharmaceutical industry. The global absolute true rate achieved by the new predictor is over 92% and its local accuracy is over 95%. Both have substantially exceeded the other existing state-of-the-art predictors. To maximize the convenience for most experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/pLoc_Deep-mAnimal/, which will become a very useful tool for fighting pandemic coronavirus and save the mankind of this planet.