Classification of Mixtures of Chinese Herbal Medicines Based on a Self‐organizing Map (SOM)

Wang, Maolin; Li, Li; Chen, Yu; Yan, Aixia; Zhao, Zhongzhen; Zhang, Ge; Jiang, Miao; Lü, Aiping; Gasteiger, Johann

doi:10.1002/minf.201500115

Cited by 24 publications

(22 citation statements)

References 22 publications

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“…Here, we discuss the novelty and contribution of the architecture structure of our proposed neural network for supervised learning (classification). Our proposed neural network model was inspired by the network in Wang et al 8 We introduce a new neuron/node structure and a learning scheme that exploit the class label in the weight update step.…”

Section: Proposed Classifiermentioning

confidence: 99%

“…In this paper, we describe a simple but effective artificial neural network method, inspired by the network that has been proposed and used in Wang et al 8 The proposed neural network has been designed to integrate and take advantage of the probability of a particular neuron to be a winner by a voting criteria to classify mammographic images.…”

Section: Introductionmentioning

confidence: 99%

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Automated Classification of Malignant and Benign Breast Cancer Lesions Using Neural Networks on Digitized Mammograms

2019

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We propose a novel neural network approach for the classification of abnormal mammographic images into benign or malignant based on their texture representations. The proposed framework has the capability of mapping high dimensional feature space into a lowerdimension, in a supervised way. The main contribution of the proposed classifier is to introduce a new neuron structure for map representation and adopt a supervised learning technique for feature classification. This is achieved by making the weight updating procedure dependent on the class reliability of the neuron. We showed high accuracy (95.2%) for our proposed approach in the classification of abnormal real mammographic images when compared to other related methods.

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Section: Proposed Classifiermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated Classification of Malignant and Benign Breast Cancer Lesions Using Neural Networks on Digitized Mammograms

2019

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“…Fu et al discovered the hot/cold herb groups associated with the target functional pathways of the chemical compounds [18]. Wang et al established a hot/cold property classifier based on the different types of the molecular descriptors of the ingredient compounds of TCM [19]. Recent studies determined the chemical features with fingerprints and ADME (absorption, distribution, metabolism, and excretion) properties for Meridian prediction.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Associations between Meridians and Chinese Traditional Medicine Using a Cost-Sensitive Graph Convolutional Neural Network

Yeh

Chao

Lai

et al. 2020

IJERPH

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Natural products are the most important and commonly used in Traditional Chinese Medicine (TCM) for healthcare and disease prevention in East-Asia. Although the Meridian system of TCM was established several thousand years ago, the rationale of Meridian classification based on the ingredient compounds remains poorly understood. A core challenge for the traditional machine learning approaches for chemical activity prediction is to encode molecules into fixed length vectors but ignore the structural information of the chemical compound. Therefore, we apply a cost-sensitive graph convolutional neural network model to learn local and global topological features of chemical compounds, and discover the associations between TCM and their Meridians. In the experiments, we find that the performance of our approach with the area under the receiver operating characteristic curve (ROC-AUC) of 0.82 which is better than the traditional machine learning algorithm and also obtains 8%–13% improvement comparing with the state-of-the-art methods. We investigate the powerful ability of deep learning approach to learn the proper molecular descriptors for Meridian prediction and to provide novel insights into the complementary and alternative medicine of TCM.

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“…Wang et al . collected 5,464 compounds for 115 herbs and applied an unsupervised clustering method called Self-organizing map (SOM) to establish a classifier of cold and hot herbs based on the chemical structural fingerprints of the compounds [29]. However, these machine learning studies focused only on the hot/cold classification of TCM herbs, while it remains unknown whether the Meridian classification that involves 12 major classes can be also predicted from the chemical structure and physiochemical features of ingredient compounds.…”

Section: Introductionmentioning

confidence: 99%

Predicting Meridian in Chinese traditional medicine using machine learning approaches

et al. 2019

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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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Classification of Mixtures of Chinese Herbal Medicines Based on a Self‐organizing Map (SOM)

Cited by 24 publications

References 22 publications

Automated Classification of Malignant and Benign Breast Cancer Lesions Using Neural Networks on Digitized Mammograms

Automated Classification of Malignant and Benign Breast Cancer Lesions Using Neural Networks on Digitized Mammograms

Predicting the Associations between Meridians and Chinese Traditional Medicine Using a Cost-Sensitive Graph Convolutional Neural Network

Predicting Meridian in Chinese traditional medicine using machine learning approaches

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