ReviewFrontiers in pharmacology2020
Unsupervised Learning and Multipartite Network Models: A Promising Approach for Understanding Traditional Medicine.
Review in Frontiers in pharmacology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A critical assessment of Traditional Chinese Medicine databases as a source for drug discovery.Frontiers in pharmacology · 2024Pooled it
- Informative Missingness in Nominal Data: A Graph-Theoretic Approach to Revealing Hidden Structure.Computational and structural biotechnology journal · 2026Article
- SysML: adaptive recommendation system for heterogeneous biomedical data preprocessing and modeling workflows.Briefings in bioinformatics · 2025Article
- Correlation of multiple peripheral blood parameters with metastasis and invasion of papillary thyroid cancer: a retrospective cohort study.Endocrine · 2025Article
- HerbComb: An integrated database for the discovery of novel combinational therapies from herbal medicines.Computational and structural biotechnology journal · 2025Article
- Compatibility optimization of the traditional Chinese medicines 'Eczema mixture' based on back-propagation artificial neural network and non-dominated sorting genetic algorithm.Frontiers in pharmacology · 2025Article
- Review
- A practical guide to implementing artificial intelligence in traditional East Asian medicine research.Integrative medicine research · 2024Article
- Herb-CMap: a multimodal fusion framework for deciphering the mechanisms of action in traditional Chinese medicine using Suhuang antitussive capsule as a case study.Briefings in bioinformatics · 2024Article
- Network pharmacology to explore the mechanism of traditional Chinese medicine in the treatment of ground glass nodules.Journal of thoracic disease · 2024Article
- Application of AI on cholangiocarcinoma.Frontiers in oncology · 2024Review
- A Novel Network Pharmacology Strategy Based on the Universal Effectiveness-Common Mechanism of Medical Herbs Uncovers Therapeutic Targets in Traumatic Brain Injury.Drug design, development and therapy · 2024Article
- Hua-Feng-Dan Alleviates LPS-induced Neuroinflammation by Inhibiting the TLR4/Myd88/NF-κB Pathway: Integrating Network Pharmacology and Experimental Validation.Current pharmaceutical design · 2024Article
- Article
- Reveal the mechanisms of prescriptions for liver cancer' treatment based on two illustrious senior TCM physicians.Journal of traditional Chinese medicine = Chung i tsa chih ying wen pan · 2023Article
- Bioactive, Mineral and Antioxidative Properties of Gluten-Free Chicory Supplemented Snack: Impact of Processing Conditions.Foods (Basel, Switzerland) · 2022Article
- Agricultural Parameters and Essential Oil Content Composition Prediction of Aniseed, Based on Growing Year, Locality and Fertilization Type-An Artificial Neural Network Approach.Life (Basel, Switzerland) · 2022Article
- Bipartite network models to design combination therapies in acute myeloid leukaemia.Nature communications · 2022Article
- Polypharmacology-based approach for screening TCM against coinfection ofFrontiers in veterinary science · 2022Article
- The Material Basis and Mechanism of Xuefu Zhuyu Decoction in Treating Stable Angina Pectoris and Unstable Angina Pectoris.Evidence-based complementary and alternative medicine : eCAM · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The ultimate goal of precision medicine is to determine right treatment for right patients based on precise diagnosis. To achieve this goal, correct stratification of patients using molecular features and clinical phenotypes is crucial. During the long history of medical science, our understanding on disease classification has been improved greatly by chemistry and molecular biology. Nowadays, we gain access to large scale patient-derived data by high-throughput technologies, generating a greater need for data science including unsupervised learning and network modeling. Unsupervised learning methods such as clustering could be a better solution to stratify patients when there is a lack of predefined classifiers. In network modularity analysis, clustering methods can be also applied to elucidate the complex structure of biological and disease networks at the systems level. In this review, we went over the main points of clustering analysis and network modeling, particularly in the context of Traditional Chinese medicine (TCM). We showed that this approach can provide novel insights on the rationale of classification for TCM herbs. In a case study, using a modularity analysis of multipartite networks, we illustrated that the TCM classifications are associated with the chemical properties of the herb ingredients. We concluded that multipartite network modeling may become a suitable data integration tool for understanding the mechanisms of actions of traditional medicine.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.