Identification of herbal categories active in pain disorder subtypes by machine learning help reveal novel molecular mechanisms of algesia
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论文类型：期刊论文
发表时间：2020-06-01
发表刊物：PHARMACOLOGICAL RESEARCH
收录刊物：PubMed、SCIE、SSCI
卷号：156
页面范围：104797
ISSN号：1043-6618
关键字：Pain disorder subtypes; Complex network analysis; Network pharmacology; Cancer pain
摘要：Chronic pain is highly prevalent and poorly controlled, of which the accurate underlying mechanisms need be further elucidated. Herbal drugs have been widely used for controlling various pain disorders. The systematic integration of pain herbal data resources might be promising to help investigate the molecular mechanisms of pain phenotypes. Here, we integrated large-scale bibliographic literatures and well-established data sources to obtain high-quality pain relevant herbal data (i.e. 426 pain related herbs with their targets). We used machine learning method to identify three distinct herb categories with their specific indications of symptoms, targets and enriched pathways, which were characterized by the efficacy of treatment to the chronic cough related neuropathic pain, the reproduction and autoimmune related pain, and the cancer pain, respectively. We further detected the novel pathophysiological mechanisms of the pain subtypes by network medicine approach to evaluate the interactions between herb targets and the pain disease modules. This work increased the understanding of the underlying molecular mechanisms of pain subtypes that herbal drugs are participating and with the ultimate aim of developing novel personalized drugs for pain disorders.
发表时间：2020-06-01