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has been cited by the following article:

• TITLE: pLoc_Deep-mGneg: Predict Subcellular Localization of Gram Negative Bacterial Proteins by Deep Learning

  AUTHORS: Xin-Xin Liu, Kuo-Chen Chou

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

  JOURNAL NAME: Advances in Bioscience and Biotechnology, Vol.11 No.5, May 11, 2020

  ABSTRACT: The recent worldwide spreading of pneumonia-causing virus, such as Coronavirus, COVID-19, and H1N1, has been endangering the life of human beings all around the world. In order to really understand the biological process within a cell level and provide useful clues to develop antiviral drugs, information of Gram negative bacterial protein subcellular localization is vitally important. In view of this, a CNN based protein subcellular localization predictor called “pLoc_Deep-mGnet” 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 98% and its local accuracy is around 94% - 100%. Both are transcending other existing state-of-the-art predictors significantly. 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-mGneg/, which will become a very useful tool for fighting pandemic coronavirus and save the mankind of this planet.