The Experts below are selected from a list of 57411 Experts worldwide ranked by ideXlab platform
Jing Tang - One of the best experts on this subject based on the ideXlab platform.
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predicting Meridian in chinese traditional medicine using machine learning approaches
PLOS Computational Biology, 2019Co-Authors: Yinyin Wang, Mohieddin Jafari, Yun Tang, Jing TangAbstract:Plant-derived nature products, known as herb formulas, have been commonly used in Traditional Chinese Medicine (TCM) for disease prevention and treatment. The herbs have been traditionally classified into different categories according to the TCM Organ systems known as Meridians. Despite the increasing knowledge on the active components of the herbs, the rationale of Meridian classification remains poorly understood. In this study, we took a machine learning approach to explore the classification of Meridian. We determined the molecule features for 646 herbs and their active components including structure-based fingerprints and ADME properties (absorption, distribution, metabolism and excretion), and found that the Meridian can be predicted by machine learning approaches with a top accuracy of 0.83. We also identified the top compound features that were important for the Meridian prediction. To the best of our knowledge, this is the first time that molecular properties of the herb compounds are associated with the TCM Meridians. Taken together, the machine learning approach may provide novel insights for the understanding of molecular evidence of Meridians in TCM.
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Predicting Meridian in Chinese Traditional Medicine Using Machine Learning Approaches
bioRxiv, 2019Co-Authors: Yinyin Wang, Mohieddin Jafari, Yun Tang, Jing TangAbstract:Abstract Plant-derived nature products, known as herb formulas, have been commonly used in Traditional Chinese Medicine (TCM) for disease prevention and treatment. The herbs have been traditionally classified into different categories according to the TCM Organ systems known as Meridians. Despite the increasing knowledge on the active components of the herbs, the rationale of Meridian classification remains poorly understood. In this study, we took a machine learning approach to explore the classification of Meridian. We determined the molecule features for 646 herbs and their active components including structure-based fingerprints and ADME properties (absorption, distribution, metabolism and excretion), and found that the Meridian can be predicted by machine learning approaches with a top accuracy of 0.83. We also identified the top compound features that were important for the Meridian prediction. To the best of our knowledge, this is the first time that molecular properties of the herb compounds are associated with the TCM Meridians. Taken together, the machine learning approach may provide novel insights for the understanding of molecular evidence of Meridians in TCM. Author Summary In East Asia, plant-derived natural products, known as herb formulas, have been commonly used as Traditional Chinese Medicine (TCM) for disease prevention and treatment. According to the theory of TCM, herbs can be classified as different Meridians according to the balance of Yin and Yang, which are commonly understood as metaphysical concepts. Therefore, the scientific rational of Meridian classification remains poorly understood. The aim of our study was to provide a computational means to understand the classification of Meridians. We showed that the Meridians of herbs can be predicted by the molecular and chemical features of the ingredient compounds, suggesting that the Meridians indeed are associated with the properties of the compounds. Our work provided a novel chemoinformatics approach which may lead to a more systematic strategy to identify the mechanisms of action and active compounds for TCM herbs.
Yinyin Wang - One of the best experts on this subject based on the ideXlab platform.
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predicting Meridian in chinese traditional medicine using machine learning approaches
PLOS Computational Biology, 2019Co-Authors: Yinyin Wang, Mohieddin Jafari, Yun Tang, Jing TangAbstract:Plant-derived nature products, known as herb formulas, have been commonly used in Traditional Chinese Medicine (TCM) for disease prevention and treatment. The herbs have been traditionally classified into different categories according to the TCM Organ systems known as Meridians. Despite the increasing knowledge on the active components of the herbs, the rationale of Meridian classification remains poorly understood. In this study, we took a machine learning approach to explore the classification of Meridian. We determined the molecule features for 646 herbs and their active components including structure-based fingerprints and ADME properties (absorption, distribution, metabolism and excretion), and found that the Meridian can be predicted by machine learning approaches with a top accuracy of 0.83. We also identified the top compound features that were important for the Meridian prediction. To the best of our knowledge, this is the first time that molecular properties of the herb compounds are associated with the TCM Meridians. Taken together, the machine learning approach may provide novel insights for the understanding of molecular evidence of Meridians in TCM.
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Predicting Meridian in Chinese Traditional Medicine Using Machine Learning Approaches
bioRxiv, 2019Co-Authors: Yinyin Wang, Mohieddin Jafari, Yun Tang, Jing TangAbstract:Abstract Plant-derived nature products, known as herb formulas, have been commonly used in Traditional Chinese Medicine (TCM) for disease prevention and treatment. The herbs have been traditionally classified into different categories according to the TCM Organ systems known as Meridians. Despite the increasing knowledge on the active components of the herbs, the rationale of Meridian classification remains poorly understood. In this study, we took a machine learning approach to explore the classification of Meridian. We determined the molecule features for 646 herbs and their active components including structure-based fingerprints and ADME properties (absorption, distribution, metabolism and excretion), and found that the Meridian can be predicted by machine learning approaches with a top accuracy of 0.83. We also identified the top compound features that were important for the Meridian prediction. To the best of our knowledge, this is the first time that molecular properties of the herb compounds are associated with the TCM Meridians. Taken together, the machine learning approach may provide novel insights for the understanding of molecular evidence of Meridians in TCM. Author Summary In East Asia, plant-derived natural products, known as herb formulas, have been commonly used as Traditional Chinese Medicine (TCM) for disease prevention and treatment. According to the theory of TCM, herbs can be classified as different Meridians according to the balance of Yin and Yang, which are commonly understood as metaphysical concepts. Therefore, the scientific rational of Meridian classification remains poorly understood. The aim of our study was to provide a computational means to understand the classification of Meridians. We showed that the Meridians of herbs can be predicted by the molecular and chemical features of the ingredient compounds, suggesting that the Meridians indeed are associated with the properties of the compounds. Our work provided a novel chemoinformatics approach which may lead to a more systematic strategy to identify the mechanisms of action and active compounds for TCM herbs.
Wei Bo Zhang - One of the best experts on this subject based on the ideXlab platform.
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comparison of extracellular and intracellular fluid distribution between stomach Meridian or gallbladder Meridian and their neighboring tissues of lower leg in healthy volunteers
Acupuncture Research, 2018Co-Authors: Guangju Wang, Peng-na Zhao, Yulong Wei, Wei Bo ZhangAbstract:OBJECTIVE: To compare differences of extracellular fluid impedance (Re) and intracellular fluid impedance (Ri) between the Stomach(ST) Meridian or Gallbladder(GB) Meridian and their neighboring non-Meridian sites of the left lower leg at the same level, so as to explore the distribution characteristics of body fluid in the Meridian. METHODS: Sixteen healthy volunteers were enrolled in the present study. The Re and Ri were detected by using Ag/AgCl electrodes and a digital lock-in amplifier. The measuring electrodes (at an interval of about 3 cm) were separately fixed to the skin sites covering the running courses of the ST Meridian (in the lateral interspace of the anterior tibial muscle)and the GB Meridian (in the interspace of the anterior edge of the fibula), and the excitation electrodes (at an interval of about 9 cm) respectively fixed to the skin sites covering the anterior tibial muscle and the interspace between the anterior tibial muscle and the tibia (about 2 cm and 5 cm lateral to the ST and GB Meridians, and about 3－4 cm and 6－8 cm lateral to the ST and GB Meridians, respectively). A 100 µA constant current with frequencies from 1 kHz to 100 kHz delivered via an excitation electrode was applied to the site (control spots of the ST Meridian), and signals of the voltage amplitude and phase difference of the tissues fed to the lock-in amplifier via the measuring electrode were collected, followed by measuring those of the GB Meridian and control sites. The circumference of the lower leg around the two excitation and measuring electrodes was measured. Then the cole-cole curve fitting was performed to calculate the Ri and Re, as well as the intracellular fluid resistivity (ρi) and extracellular fluid resistivity (ρe) of the ST and GB Meridians, the related muscles and interspace lateral to ST or GB (ST/GB) Meridians at the same level. RESULTS: The Ri and Re (Ω) values of the ST, GB, the muscle lateral to ST/GB and the interspace lateral to ST/GB were 19.1±1.3 and 28.3±1.4, 15.8±1.9 and 25.7±2.0, 19.6±1.3 and 31.3±1.6, and 19.4±1.2 and 32.4±1.6, respectively. The Re values were significantly lower at the ST and GB Meridians than at the muscle lateral to and the interspace lateral to both Meridians (P<0.05). The ρi and ρe values (Ω•cm) of the ST, GB, the muscle lateral to and the interspace lateral to ST/GB were 658.9±78.5 and 953.8±75.3, 528.0±90.1 and 833.9±101.7, 669.9±71.8 and 1 059.8±86.0, 655.9±64.8 and 1 099.3±93.3, respectively. The ρi and ρe values were significantly lower at the GB Meridian Than at the other three locntions, and the ρe value of ST Meridian was significantly lower than those of the muscle lateral to and the interspace lateral to ST/GB Meridians (P<0.01)．. CONCLUSION: The Ri, Re, ρi and ρe values of the ST and GB Meridians are significantly lower than those of their neighboring tissues at the same levels of the lower leg, suggesting a more extracellular fluid in the Meridian running course and providing evidence for our speculation that the Meridian is a hydraulic resistance channel.
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classic and modern Meridian studies a review of low hydraulic resistance channels along Meridians and their relevance for therapeutic effects in traditional chinese medicine
Evidence-based Complementary and Alternative Medicine, 2015Co-Authors: Wei Bo Zhang, Guangjun Wang, Kjell FuxeAbstract:Meridian theory is one of the core components of the theory of traditional Chinese medicine (TCM). It gives an integral explanation for how human life works, how a disease forms, and how a therapy acts to treat a disease. If we do not understand the Meridians, it is hard to understand the TCM. People in China and abroad had been working hard for 50 years, trying to understand the Meridians; then 15 years ago a breakthrough idea appeared when we realized that they are low resistance fluid channels where various chemical and physical transports take place. The channel is called low hydraulic resistance channel (LHRC) and the chemical transport is named volume transmission (VT). This review aims to give a full understanding of the essence of Meridian and its works on the therapies of TCM.
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Comparison of Acupuncture Effect on Blood Perfusion between Needling Nonacupoint on Meridian and Needling Nonacupoint off Meridian
Evidence-based Complementary and Alternative Medicine, 2013Co-Authors: Wei Bo Zhang, Hong Li, Ling-ling Wang, Yuying TianAbstract:To verify the ancient theory of rather missing the acupoint than missing the Meridian, acupuncture at nonacupoint on Meridian and acupuncture at nonacupoint off Meridian were performed, respectively. The blood perfusion (BP) on the calf around bladder Meridian area was measured with a laser Doppler perfusion imager before, during, and after acupuncture. The whole scanning field was divided into seven subareas, and mean BP on each area was calculated. The ratio of mean BP between a subarea and a reference subarea was gotten, and then the change rate was calculated as ratio change rate (RCR). The results showed that RCR on bladder Meridian area and around Chengshan (BL57) during or after acupuncture at nonacupoint on Meridian was significantly higher than that at nonacupoint off Meridian, which supports the ancient theory. Such differences may be attributable to some factors that can facilitate the signals transmission and produce a better acupuncture effect, such as richer nerve terminals, blood vessels, and mast cells which can produce stronger signals on the acupoints and the low hydraulic resistance channel along Meridians which plays a role of signal transmitting channel to get a better effect of acupuncture.
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Meridian Studies in China: A Systematic Review
Journal of acupuncture and meridian studies, 2010Co-Authors: Guangjun Wang, M. Hossein Ayati, Wei Bo ZhangAbstract:Meridian theory is a major part of Chinese medicine and has guided acupuncture and clinical practice for thousands of years. Meridian theory describes many important concepts about the rules of human body function and regulation, but has comparatively huge differences with the basic concepts of modern medicine. These differences have caused deep concern and attracted attention from scholars, both inside and outside of China. The interest in Meridian theory lies in determining the structural nature of Meridians. Not only is this information still unclear, it is very difficult to achieve clear results in a short period of time. Despite this, the phenomena of Meridians can be used as the entry point for Meridian studies. After many years of effort, although the physical structure of Meridians has not been found, the existence of the Meridian phenomena has been fully confirmed. Although there is a lack of morphological evidence for the existence of the Meridian, concluding non-existence may be incorrect as morphology techniques develop and structures previously not determined are being found. Since the phenomenon of Meridians exists, some biological basis behind its occurrence must be present. This implies that research on Meridians needs to continue as research techniques advance and may eventually reveal the biological basis of the Meridian phenomenon. In the present review, we analyze the history of Meridian studies in China.
Mohieddin Jafari - One of the best experts on this subject based on the ideXlab platform.
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predicting Meridian in chinese traditional medicine using machine learning approaches
PLOS Computational Biology, 2019Co-Authors: Yinyin Wang, Mohieddin Jafari, Yun Tang, Jing TangAbstract:Plant-derived nature products, known as herb formulas, have been commonly used in Traditional Chinese Medicine (TCM) for disease prevention and treatment. The herbs have been traditionally classified into different categories according to the TCM Organ systems known as Meridians. Despite the increasing knowledge on the active components of the herbs, the rationale of Meridian classification remains poorly understood. In this study, we took a machine learning approach to explore the classification of Meridian. We determined the molecule features for 646 herbs and their active components including structure-based fingerprints and ADME properties (absorption, distribution, metabolism and excretion), and found that the Meridian can be predicted by machine learning approaches with a top accuracy of 0.83. We also identified the top compound features that were important for the Meridian prediction. To the best of our knowledge, this is the first time that molecular properties of the herb compounds are associated with the TCM Meridians. Taken together, the machine learning approach may provide novel insights for the understanding of molecular evidence of Meridians in TCM.
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Predicting Meridian in Chinese Traditional Medicine Using Machine Learning Approaches
bioRxiv, 2019Co-Authors: Yinyin Wang, Mohieddin Jafari, Yun Tang, Jing TangAbstract:Abstract Plant-derived nature products, known as herb formulas, have been commonly used in Traditional Chinese Medicine (TCM) for disease prevention and treatment. The herbs have been traditionally classified into different categories according to the TCM Organ systems known as Meridians. Despite the increasing knowledge on the active components of the herbs, the rationale of Meridian classification remains poorly understood. In this study, we took a machine learning approach to explore the classification of Meridian. We determined the molecule features for 646 herbs and their active components including structure-based fingerprints and ADME properties (absorption, distribution, metabolism and excretion), and found that the Meridian can be predicted by machine learning approaches with a top accuracy of 0.83. We also identified the top compound features that were important for the Meridian prediction. To the best of our knowledge, this is the first time that molecular properties of the herb compounds are associated with the TCM Meridians. Taken together, the machine learning approach may provide novel insights for the understanding of molecular evidence of Meridians in TCM. Author Summary In East Asia, plant-derived natural products, known as herb formulas, have been commonly used as Traditional Chinese Medicine (TCM) for disease prevention and treatment. According to the theory of TCM, herbs can be classified as different Meridians according to the balance of Yin and Yang, which are commonly understood as metaphysical concepts. Therefore, the scientific rational of Meridian classification remains poorly understood. The aim of our study was to provide a computational means to understand the classification of Meridians. We showed that the Meridians of herbs can be predicted by the molecular and chemical features of the ingredient compounds, suggesting that the Meridians indeed are associated with the properties of the compounds. Our work provided a novel chemoinformatics approach which may lead to a more systematic strategy to identify the mechanisms of action and active compounds for TCM herbs.
Yun Tang - One of the best experts on this subject based on the ideXlab platform.
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predicting Meridian in chinese traditional medicine using machine learning approaches
PLOS Computational Biology, 2019Co-Authors: Yinyin Wang, Mohieddin Jafari, Yun Tang, Jing TangAbstract:Plant-derived nature products, known as herb formulas, have been commonly used in Traditional Chinese Medicine (TCM) for disease prevention and treatment. The herbs have been traditionally classified into different categories according to the TCM Organ systems known as Meridians. Despite the increasing knowledge on the active components of the herbs, the rationale of Meridian classification remains poorly understood. In this study, we took a machine learning approach to explore the classification of Meridian. We determined the molecule features for 646 herbs and their active components including structure-based fingerprints and ADME properties (absorption, distribution, metabolism and excretion), and found that the Meridian can be predicted by machine learning approaches with a top accuracy of 0.83. We also identified the top compound features that were important for the Meridian prediction. To the best of our knowledge, this is the first time that molecular properties of the herb compounds are associated with the TCM Meridians. Taken together, the machine learning approach may provide novel insights for the understanding of molecular evidence of Meridians in TCM.
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Predicting Meridian in Chinese Traditional Medicine Using Machine Learning Approaches
bioRxiv, 2019Co-Authors: Yinyin Wang, Mohieddin Jafari, Yun Tang, Jing TangAbstract:Abstract Plant-derived nature products, known as herb formulas, have been commonly used in Traditional Chinese Medicine (TCM) for disease prevention and treatment. The herbs have been traditionally classified into different categories according to the TCM Organ systems known as Meridians. Despite the increasing knowledge on the active components of the herbs, the rationale of Meridian classification remains poorly understood. In this study, we took a machine learning approach to explore the classification of Meridian. We determined the molecule features for 646 herbs and their active components including structure-based fingerprints and ADME properties (absorption, distribution, metabolism and excretion), and found that the Meridian can be predicted by machine learning approaches with a top accuracy of 0.83. We also identified the top compound features that were important for the Meridian prediction. To the best of our knowledge, this is the first time that molecular properties of the herb compounds are associated with the TCM Meridians. Taken together, the machine learning approach may provide novel insights for the understanding of molecular evidence of Meridians in TCM. Author Summary In East Asia, plant-derived natural products, known as herb formulas, have been commonly used as Traditional Chinese Medicine (TCM) for disease prevention and treatment. According to the theory of TCM, herbs can be classified as different Meridians according to the balance of Yin and Yang, which are commonly understood as metaphysical concepts. Therefore, the scientific rational of Meridian classification remains poorly understood. The aim of our study was to provide a computational means to understand the classification of Meridians. We showed that the Meridians of herbs can be predicted by the molecular and chemical features of the ingredient compounds, suggesting that the Meridians indeed are associated with the properties of the compounds. Our work provided a novel chemoinformatics approach which may lead to a more systematic strategy to identify the mechanisms of action and active compounds for TCM herbs.
