Multi-Omics Data Fusion and Artificial Intelligence for Phylogenetic Classification of Medicinal Plant Resources

Abstract

Medicinal plant resources are central to pharmaceutical innovation, traditional medicine, and biodiversity conservation, yet their phylogenetic classification remains complicated by cryptic speciation, hybridization, phenotypic plasticity, and incomplete voucher-linked reference collections. This paper examines how multi-omics data fusion combined with artificial intelligence can transform phylogenetic classification from a narrow molecular barcoding task into a systems-level inference problem. It analyzes the structural trade-offs among early, intermediate, and late data fusion architectures and discusses how genomic, transcriptomic, metabolomic, and cell wall compositional features can be jointly represented for downstream classification. The discussion emphasizes system requirements such as provenance-aware data pipelines, modular model design, uncertainty quantification, validation across heterogeneous plant lineages, and continuous monitoring after deployment. The paper argues that technical accuracy alone is insufficient. Fairness, benefit-sharing, ecological sustainability, and policy coherence must be designed into the infrastructure rather than appended after model training. Drawing on cross-domain comparisons from biomedical and ecological data science, the paper provides a forward-looking perspective on resilient, governable, and ethically grounded computational infrastructure for medicinal plant phylogenetics. The conclusion highlights the need for international data governance frameworks, reproducible model registries, and participatory approaches that include Indigenous and local knowledge systems in the curation and interpretation of medicinal plant data.