Year: 2025
Type: conference-paper
Abstract: 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.
Authors: Qi Zhao, Juan Wang, Wenyuan Gao
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
OpenAlex ID: W4413577451