Fun(gi)omics: Advanced and Diverse Technologies to Explore Emerging Fungal Pathogens and Define Mechanisms of Antifungal Resistance
Bibliographic record
Abstract
The landscape of infectious fungal agents includes previously unidentified or rare pathogens with the potential to cause unprecedented casualties in biodiversity, food security, and human health. The influences of human activity, including the crisis of climate change, along with globalized transport, are underlying factors shaping fungal adaptation to increased temperature and expanded geographical regions. Furthermore, the emergence of novel antifungal-resistant strains linked to excessive use of antifungals (in the clinic) and fungicides (in the field) offers an additional challenge to protect major crop staples and control dangerous fungal outbreaks. Hence, the alarming frequency of fungal infections in medical and agricultural settings requires effective research to understand the virulent nature of fungal pathogens and improve the outcome of infection in susceptible hosts. Mycology-driven research has benefited from a contemporary and unified approach of omics technology, deepening the biological, biochemical, and biophysical understanding of these emerging fungal pathogens. Here, we review the current state-of-the-art multi-omics technologies, explore the power of data integration strategies, and highlight discovery-based revelations of globally important and taxonomically diverse fungal pathogens. This information provides new insight for emerging pathogens through an in-depth understanding of well-characterized fungi and provides alternative therapeutic strategies defined through novel findings of virulence, adaptation, and resistance.
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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 | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".