TCMFP: a novel herbal formula prediction method based on network target's score integrated with semi-supervised learning genetic algorithms

In this study, we propose a herbal formula prediction approach (TCMFP) combined therapy experience of TCM, artificial intelligence and network science algorithms to screen optimal herbal formula for diseases efficiently, which integrates a herb score (Hscore) based on the importance of network targets, a pair score (Pscore) based on empirical learning and herbal formula predictive score (FmapScore) based on intelligent optimization and genetic algorithm. The validity of Hscore, Pscore and FmapScore was verified by functional similarity and network topological evaluation. Moreover, TCMFP was used successfully to generate herbal formulae for three diseases, i.e. the Alzheimer's disease, asthma and atherosclerosis. Functional enrichment and network analysis indicates the efficacy of targets for the predicted optimal herbal formula. The proposed TCMFP may provides a new strategy for the optimization of herbal formula, TCM herbs therapy and drug development.PMID:36941113 | DOI:10.1093/bib/bbad102

Source: Briefings in Bioinformatics - March 20, 2023 Category: Bioinformatics Authors: Qikai Niu Hongtao Li Lin Tong Sihong Liu Wenjing Zong Siqi Zhang SiWei Tian Jingai Wang Jun Liu Bing Li Zhong Wang Huamin Zhang Source Type: research