Opportunities and Challenges of Multiomics for Discovery and Monitoring of Human Pathogens
Bibliographic record
Abstract
Detection and monitoring of pathogens is a central aspect of maintaining public health. Rare and neglected zoonotic viruses have the potential to evolve and expand exponentially, leading to unforeseen outbreaks, epidemics, and pandemics. The emerging multiomics and meta-omics techniques and workflows, such as proteogenomics and meta-genomics, offer the potential for the detection of harmful pathogens, as well as opportunities for the discovery of previously unknown bacterial, parasitic, or viral pathogens. Multiomics and meta-omics workflows provide molecular information for tracking pathogens and understanding the effectiveness of spread mitigation strategies. In addition to environmental monitoring, multiomics and meta-omics approaches have the potential for clinical applications and in-depth characterization of novel pathogens. In this review, we discuss recent applications of multiomics and meta-omics techniques, their advantages over traditional methods, and their potential implementations in biomedical research, environmental studies, and healthcare. We critically assess the benefits and challenges of multiomics and meta-omics studies and discuss their future perspectives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".