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Machine learning for synergistic network pharmacology: a comprehensive overview
Briefings Bioinform., 2023Network pharmacology is an emerging area of systematic drug research that attempts to understand drug actions and interactions with multiple targets. Network pharmacology has changed the paradigm from 'one-target one-drug' to highly potent 'multi-target ...
F. Noor +4 more
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Application prospect of traditional Chinese medicine network pharmacology in food science research.
The Journal of the Science of Food and Agriculture, 2023In-depth research on the mechanism of food nutrition and bioactivity has always been a shortcoming. The main function of the food itself is to meet the nutritional needs of the human body, not the therapeutic effect.
Sui Yuanyuan +3 more
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Integration strategy of network pharmacology in Traditional Chinese Medicine: a narrative review.
Journal of traditional Chinese medicine = Chung i tsa chih ying wen pan, 2022Traditional Chinese Medicine (TCM) has been extensively used as a mainstay for treating various pathologies. Combing the pharmacology and systems biology approaches, the network pharmacology (NP) approach was developed to predict the probable mechanism ...
W. U. Jiashuo +4 more
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Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica, 2022
Traditional Chinese medicine(TCM) has unique advantages in the prevention and treatment of diseases owing to its holistic view and more than 2 000 years of experience in the clinical use of natural medicine.
Zi-Yi Wang +4 more
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Traditional Chinese medicine(TCM) has unique advantages in the prevention and treatment of diseases owing to its holistic view and more than 2 000 years of experience in the clinical use of natural medicine.
Zi-Yi Wang +4 more
semanticscholar +1 more source
Information Loss in Network Pharmacology
Molecular Informatics, 2019AbstractWith the advent of increasing computational power and large‐scale data acquisition, network analysis has become an attractive tool to study the organisation of complex systems and the interrelation of their constituent entities in various scientific domains.
Ingo Vogt, Jordi Mestres
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“Network Target” Theory and Network Pharmacology
2021In the biomedical big data and artificial intelligence era, pioneering interdisciplinary information science research, life science, and medicine are represented by a complex biological network which has attracted increasing attention from researchers.
Qingyang Ding, Shao Li, Xin Wang
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Food & Function, 2022
Alismatis rhizoma (AR), the dried rhizome of Alisma orientale (Sam) Juzep, is effective in treating hyperlipidemia, but the mechanisms involved require further exploration.
Pan Yan +7 more
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Alismatis rhizoma (AR), the dried rhizome of Alisma orientale (Sam) Juzep, is effective in treating hyperlipidemia, but the mechanisms involved require further exploration.
Pan Yan +7 more
semanticscholar +1 more source
Common Network Pharmacology Databases
2021Network pharmacology research is set against the background of vast biological databases and artificial intelligence. Traditional Chinese Medicine (TCM) involves ancient manuscripts, documents, and innumerable prescriptions belonging to various dynasties.
Feifei Guo, Yanqiong Zhang, Haiyu Xu
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The impact of network biology in pharmacology and toxicology
SAR and QSAR in Environmental Research, 2012With the need to investigate alternative approaches and emerging technologies in order to increase drug efficacy and reduce adverse drug effects, network biology offers a novel way of approaching drug discovery by considering the effect of a molecule and protein's function in a global physiological environment.
Panagiotou, G.; Taboureau, O.
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Common Network Pharmacology Software
2021As a growing trend in current pharmacology research and an important medical application in network science research, network pharmacology has become an indispensable complement to traditional pharmacology research with the immense accumulation and integration of large-scale pharmacology and disease molecular network data.
Xuezhong Zhou, Ning Wang, Xing Zhai
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