TCMFP: a novel herbal formula prediction method based on network target's score integrated with semi-supervised learning genetic algorithms.
Brief Bioinform
; 24(3)2023 05 19.
Article
de En
| MEDLINE
| ID: mdl-36941113
Traditional Chinese medicine (TCM) has accumulated thousands years of knowledge in herbal therapy, but the use of herbal formulas is still characterized by reliance on personal experience. Due to the complex mechanism of herbal actions, it is challenging to discover effective herbal formulas for diseases by integrating the traditional experiences and modern pharmacological mechanisms of multi-target interactions. 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.
Mots clés
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Asthme
/
Médicaments issus de plantes chinoises
Type d'étude:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
Limites:
Humans
Langue:
En
Journal:
Brief Bioinform
Sujet du journal:
BIOLOGIA
/
INFORMATICA MEDICA
Année:
2023
Type de document:
Article
Pays d'affiliation:
Chine
Pays de publication:
Royaume-Uni