Integration of Multi-Omics Data and Machine Learning for Engineering Plant Biosensors: A Conceptual Bioinformatics Framework from Data to Design

Plant biosensor engineering currently relies on empirical, single-target strategies, limiting predictability and scalability. This review presents a comprehensive, data-driven framework that integrates multi-omics, machine learning, and synthetic biology to enable rational biosensor design. A systematic analysis of 1,548 peer-reviewed publications (2020–2026) reveals exponential growth—from 90 papers in 2020 to 646 in 2026 (7.2-fold increase)—with 70% of the corpus published between 2024 and 2026. An additional 40 pre-2020 foundational references are included in the full validated bibliography of 1,588 records. Bibliometric clustering identifies three major research poles: molecular sensors, omics integration, and AI-driven design. Analysis of real multi-omics datasets (GSE206890, PXD024902, PXD030428, PXD072721) from Arabidopsis, rice, and maize consistently shows up-regulation of ABA signaling, reactive oxygen species metabolism, and transcription factor networks. Building on these findings, the authors propose PlantBioSense-DF, a modular ten-step architecture covering data acquisition, quality control, harmonization, feature engineering, machine learning, explainable AI (SHAP, LIME), biomarker discovery, and synthetic biology circuit design. The framework prioritizes interpretable biomarkers for direct translation to engineering. Key challenges addressed include data heterogeneity, batch effects, model interpretability, and laboratory-to-field transfer. Future directions highlight digital twins, foundation models, graph AI, and single-cell/spatial omics integration. Together, this work offers a systematic, evidence-based roadmap for accelerating plant biosensor development through predictive, data-driven approaches. All analysis scripts and processed data will be made publicly available to ensure reproducibility and foster community adoption of the framework.