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

Shao, Yu-Tao and Liu, Xin-Xin and Lu, Zhe and Chou, Kuo-Chen (2020) pLoc_Deep-mHum: Predict Subcellular Localization of Human Proteins by Deep Learning. Natural Science, 12 (07). pp. 526-551. ISSN 2150-4091

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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.

Item Type: Article
Subjects: Eprint Open STM Press > Medical Science
Depositing User: Unnamed user with email admin@eprint.openstmpress.com
Date Deposited: 08 Nov 2023 09:04
Last Modified: 08 Nov 2023 09:04
URI: http://library.go4manusub.com/id/eprint/1616

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