With the rapid advancement of artificial intelligence (AI) technology, it has significantly impacted various fields within bioinformatics and functional genomics. Its ability to process vast datasets with high efficiency has led to groundbreaking discoveries in medicinal plant research. The integration of AI with functional genomics shifts the study paradigm from traditional experimental-driven approaches to data-driven and intelligent design strategies. This innovative shift enables AI to enhance the analysis of complex metabolic pathways, particularly biosynthetic pathways, by leveraging their large-scale data integration capabilities. Additionally, the application of multi-omics techniques has been integrated seamlessly, facilitating advancements in understanding gene functions, metabolic networks optimization, and breeding processes.
Currently, the accuracy of functional annotation using deep learning models (e.g., DeepGO, ChaGPT, and Deepseek) has been elevated to over 90%. Convolutional neural networks (CNN) and graph neural networks (GNN) are employed to analyze and mine unknown genes in specific metabolic branches. Additionally, AI algorithms can predict unknown metabolic pathways, such as PathPred, by identifying key enzymes that regulate secondary metabolites. These advancements facilitate the functional optimization of gene editing targets through CRISPR-Cas9, resulting in efficient knockouts of biosynthesis-related genes. AI platforms (e.g., OmicsNet) integrate diverse genomic, transcriptomic, and metabolomic data to optimize secondary metabolite synthesis networks for stress-related gene prediction and development. This topic underscores the transformative role of AI in advancing medicinal plant science, aligning with the objective of enhancing the precision and efficiency of functional annotation through these sophisticated computational techniques.
This research topic focuses on utilizing AI-driven bioinformatics and functional genomics technologies to explore medicinal plant genome annotation, metabolic pathway prediction, gene editing design, molecular marker development, stress resistance-related genes identification, and other aspects. Based on a comprehensive algorithm that combines big data analysis with the latest biotechnologies, functional genomics demonstrates how AI empowers medicinal plant research, fostering the modernization and sustainable development of traditional medicine.
1. AI-based genome annotation and functional prediction
2. AI-assisted reconstruction of metabolic pathways and gene mining
3. Deep Learning in transcriptome data processing and analysis
4. AI-driven network analysis and identification of stress-related genes
5. Integration of multi-omics data and optimization of metabolic networks
6. Artificial Intelligence prediction of gene regulatory networks
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Keywords: artificial intelligence, bioinformatics, functional genomics, medicinal plants, multi-omics integration
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