Systems Biology Approaches for Host–Fungal Interactions: An Expanding Multi-Omics Frontier
Bibliographic record
Abstract
Opportunistic fungal infections are an increasing threat for global health, and for immunocompromised patients in particular. These infections are characterized by interaction between fungal pathogen and host cells. The exact mechanisms and the attendant variability in host and fungal pathogen interaction remain to be fully elucidated. The field of systems biology aims to characterize a biological system, and utilize this knowledge to predict the system's response to stimuli such as fungal exposures. A multi-omics approach, for example, combining data from genomics, proteomics, metabolomics, would allow a more comprehensive and pan-optic "two systems" biology of both the host and the fungal pathogen. In this review and literature analysis, we present highly specialized and nascent methods for analysis of multiple -omes of biological systems, in addition to emerging single-molecule visualization techniques that may assist in determining biological relevance of multi-omics data. We provide an overview of computational methods for modeling of gene regulatory networks, including some that have been applied towards the study of an interacting host and pathogen. In sum, comprehensive characterizations of host-fungal pathogen systems are now possible, and utilization of these cutting-edge multi-omics strategies may yield advances in better understanding of both host biology and fungal pathogens at a systems scale.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".