Integrative Omics and AI-Driven Systems Biology: Multilayer Networks Decoding <i>Apis mellifera</i> Health and Resilience
Bibliographic record
Abstract
) are vital pollinators essential for maintaining ecosystem stability and global food production, but they face escalating threats from pathogens, agrochemicals, and climate change. Although proteomics has advanced our understanding of bee physiology, single-omics approaches are insufficient to capture the complexity of colony health. This review highlights the rise of integrative multiomics frameworks─combining proteomics, metabolomics, and lipidomics─with artificial intelligence (AI)-based strategies to decode molecular resilience in bees. We summarize recent advances in omics technologies, including spatial and single-cell platforms, mass spectrometry innovations, and customized computational pipelines. Furthermore, we highlight how AI-enhanced multiomics integration facilitates biomarker discovery, elucidates regulatory networks, especially in nonmodel organisms like honey bees. Emerging computational methods such as deep learning, graph neural networks, and multilayer network models offer predictive, scalable, and interpretable insights. Despite challenges like limited sample input and cross-omics heterogeneity, the convergence of omics and machine learning represents a transformative paradigm for decoding complex biological systems. These integrative approaches offer not only a deeper molecular understanding of bee biology but also generalizable frameworks for systems biology in other ecologically relevant species.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".