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has been cited by the following article:

• TITLE: pLoc_Deep-mHum: Predict Subcellular Localization of Human Proteins by Deep Learning

  AUTHORS: Yu-Tao Shao, Xin-Xin Liu, Zhe Lu, Kuo-Chen Chou

  KEYWORDS: Coronavirus; Multi-Label System; Human Proteins; Deep Learning; Five-Steps Rule; PseAAC

  JOURNAL NAME: Natural Science, Vol.12 No.7, July 27, 2020

  ABSTRACT: Recently, the life of human beings around the entire world has been endangering by the spreading of pneumonia-causing virus, such as Coronavirus, COVID-19, and H1N1. To develop effective drugs against Coronavirus, knowledge of protein subcellular localization is indispensable. In 2019, a predictor called “pLoc_bal-mHum” was developed for identifying the subcellular localization of human proteins. Its predicted results are significantly better than its counterparts, particularly for those proteins that may simultaneously occur or move between two or more subcellular location sites. However, more efforts are definitely needed to further improve its power since pLoc_bal-mHum was still not trained by a “deep learning”, a very powerful technique developed recently. The present study was devoted to incorporate the “deep-learning” technique and develop a new predictor called “pLoc_Deep-mHum”. The global absolute true rate achieved by the new predictor is over 81% and its local accuracy is over 90%. Both are overwhelmingly superior to its counterparts. Moreover, a user-friendly web-server for the new predictor has been well established at http://www.jci-bioinfo.cn/pLoc_Deep-mHum/, which will become a very useful tool for fighting pandemic coronavirus and save the mankind of this planet.