NDLI: Integrating herb effect similarity for network-based herb target prediction

Content Provider	IEEE Xplore Digital Library
Author	Yang, Kuo Zhou, Xuezhong Zhang, Runshun Liu, Baoyan Lei, Lei Zhang, Xiaoping Chu, Hongwei Sun, Changkai Gao, Zhuye Xu, Hao
Copyright Year	2015
Description	Author affiliation: School of Computer and Information Technology and Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, 100044, China (Yang, Kuo; Zhou, Xuezhong) \|\| Liaoning Provincial Key Laboratory of Cerebral Diseases, Institute for Brain Disorders Dalian Medical University, Dalian, 116044, China (Chu, Hongwei; Sun, Changkai) \|\| Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, 100700, China (Liu, Baoyan; Lei, Lei) \|\| Guanganmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China (Zhang, Runshun) \|\| Xiyuan Hospital, China Academy of China Academy of Chinese Medical Sciences, Beijing, 100091, China (Gao, Zhuye; Xu, Hao) \|\| Data Center of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, 100700, China (Zhang, Xiaoping)
Abstract	Network pharmacology has become the new approach for drug mechanism research and novel drug design. Drug target prediction based on computational approach became one of the primary approaches. However, due to the diversity and complexity of herbal chemical structures, the performance of herb target prediction based on chemical structure similarity is limited by the quality and the data availability of herb chemical ingredients and their structural properties. To gain insights into the molecular mechanism of herbs by using clinical herb efficacies, we develop a computational approach to predict the potential targets of herbs by integrating the herb effect properties. We found that herbs with high effect similarities have high degree of shared targets. Meanwhile, an algorithm integrating propagation on protein-protein interaction network and effect-based herb similarity was proposed and obtained better accuracy than the chemical structure similarity. Furthermore, we manually evaluated some novel predictions like the target SP1 for herb turmeric, which is not recorded in the benchmark set, but has been confirmed by recent published paper.
Starting Page	483
Ending Page	488
File Size	1700605
Page Count	6
File Format	PDF
e-ISBN	9781509000227
DOI	10.1109/BMEI.2015.7401553
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Proteins Drugs Correlation Databases network pharmacology link prediction Prediction algorithms herb target prediction Chemicals Diseases
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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