TITLE:
iATC_Deep-mISF: A Multi-Label Classifier for Predicting the Classes of Anatomical Therapeutic Chemicals by Deep Learning
AUTHORS:
Zhe Lu, Kuo-Chen Chou
KEYWORDS:
Pandemic Coronavirus, Multi-Label System, Anatomical Therapeutic Chemicals, Learning at Deeper Level, Five-Steps Rule
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. 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.