ArticleComputational and mathematical methods in medicine2013
Application of genetic algorithm for discovery of core effective formulae in TCM clinical data.
Article in Computational and mathematical methods in medicine, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- TCMNPAS: a comprehensive analysis platform integrating network formulaology and network pharmacology for exploring traditional Chinese medicine.Chinese medicine · 2024Article
- Data Mining, Network Pharmacology, and Molecular Docking Explore the Effects of Core Traditional Chinese Medicine Prescriptions in Patients with Rectal Cancer and Qi and Blood Deficiency Syndrome.Evidence-based complementary and alternative medicine : eCAM · 2021Article
- FangNet: Mining herb hidden knowledge from TCM clinical effective formulas using structure network algorithm.Computational and structural biotechnology journal · 2021Article
- Ingredients, Anti-Liver Cancer Effects and the Possible Mechanism of DWYG Formula Based on Network Prediction.OncoTargets and therapy · 2020Article
- Effects and possible mechanism of Ruyiping formula application to breast cancer based on network prediction.Scientific reports · 2019Article
- The Common Prescription Patterns Based on the Hierarchical Clustering of Herb-Pairs Efficacies.Evidence-based complementary and alternative medicine : eCAM · 2016Article
- An analysis of chemical ingredients network of Chinese herbal formulae for the treatment of coronary heart disease.PloS one · 2015Article
- Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective.Evidence-based complementary and alternative medicine : eCAM · 2015Review
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Authors and funding
9 authors.
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Abstract
Research on core and effective formulae (CEF) does not only summarize traditional Chinese medicine (TCM) treatment experience, it also helps to reveal the underlying knowledge in the formulation of a TCM prescription. In this paper, CEF discovery from tumor clinical data is discussed. The concepts of confidence, support, and effectiveness of the CEF are defined. Genetic algorithm (GA) is applied to find the CEF from a lung cancer dataset with 595 records from 161 patients. The results had 9 CEF with positive fitness values with 15 distinct herbs. The CEF have all had relative high average confidence and support. A herb-herb network was constructed and it shows that all the herbs in CEF are core herbs. The dataset was divided into CEF group and non-CEF group. The effective proportions of former group are significantly greater than those of latter group. A Synergy index (SI) was defined to evaluate the interaction between two herbs. There were 4 pairs of herbs with high SI values to indicate the synergy between the herbs. All the results agreed with the TCM theory, which demonstrates the feasibility of our approach.
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