Progress in multi-omics studies of ginseng using computer technology

Ginseng, renowned for bioactive ginsenosides, faces industrial scalability challenges. This review highlights computational biology's role in ginseng polyomics research. Genomics leverages PacBio HiFi and Nanopore sequencing with Hi-C scaffolding for chromosome-level assembly. Tools like RepeatModeler and OrthoFinder identify gene expansions (e.g., cytochrome P450) and positively selected alleles in ginsenoside pathways. Machine learning-based GWAS predicts growth and saponin traits for marker-assisted breeding. Transcriptomics uses scRNA-seq and spatial techniques to map cellular heterogeneity. Seurat and SCENIC uncover tissue-specific regulatory modules governing biosynthesis. Dynamic time warping tracks gene activation under stress, while CNNs predict gene function with 85% accuracy. Proteomics advances rely on DIA-MS and tools like MaxQuant. STRING and OmicsBean identify protein complexes for glycosylation and stress response. GPU-driven molecular dynamics elucidate ginsenoside-protein interactions for drug design. Key challenges include integrating multi-omics data, requiring GNN frameworks. Future research will focus on AI-driven synthetic biology (e.g., Transformer models) to optimize ginsenoside production in engineered systems. Cloud platforms will enable global data collaboration, and XAI will decipher regulatory networks, transforming ginseng into a scalable precision medicine source.