Systematically Revealing Quantitative Multi-Target Integrative Effects of Plants With Artificial Intelligence Method

Many plants have multiple chemical components and multiple targets, and their potential effects on diseases are the integrative effects of multiple targets. How to systematically reveal the integrated multi-targets effect of plants on diseases is not only a challenge, but also an innovation. This study developed a novel research method based on artificial intelligence and took hawthorn as an example; a deep auto-encoding neural network model was used to encode the expression levels of multiple common targets between hawthorn and atherosclerosis in each cell of the single-cell transcriptome of atherosclerotic perivascular adipose tissue (PVAT) as an integrated value (MTIS). The landscape and quantitative mapping of multi-targets potential integrated effect of plants on disease at the single-cell level would be achieved based on this innovative approach, and in-depth analysis such as MTIS comparisons, MTIS-pseudotime difference analysis, cell communication analysis, and immune infiltration analysis, was performed to reveal the potential mechanism and landscapes of hawthorn on the PVAT microenvironment of atherosclerotic. Due to many plants for disease having multiple chemical compositions and multiple targets, the novel method proposed in this study may have a wide range of applications.