iATC_Deep-mISF: A Multi-Label Classifier for Predicting the Classes of Anatomical Therapeutic Chemicals by Deep Learning

Lu, Zhe and Chou, Kuo-Chen (2020) iATC_Deep-mISF: A Multi-Label Classifier for Predicting the Classes of Anatomical Therapeutic Chemicals by Deep Learning. Advances in Bioscience and Biotechnology, 11 (05). pp. 153-159. ISSN 2156-8456

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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. To provide useful clues for developing antiviral drugs, information of anatomical therapeutic chemicals is vitally important. In view of this, a CNN based predictor called “iATC_Deep-mISF” has been developed. The predictor is particularly useful in dealing with the multi-label systems in which some chemicals may occur in two or more different classes. 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/iATC_Deep-mISF/, which will become a very powerful tool for developing effective drugs to fight pandemic coronavirus and save the mankind of this planet.

Item Type: Article
Subjects: Opene Prints > Biological Science
Depositing User: Managing Editor
Date Deposited: 08 Mar 2023 08:17
Last Modified: 16 Jul 2024 06:54
URI: http://geographical.go2journals.com/id/eprint/886

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