Host network-based discovery of critical regulators of innate immunity, virus growth, and pathogenesis in influenza virus infection
Bibliographic record
Abstract
ABSTRACT Innate immunity is protective against viruses, but also can facilitate pathological infection responses. Despite intensive research, our understanding of the mechanisms that regulate innate immunity in virus infection remains incomplete. Systems biology-based data-driven modeling approaches hold substantial promise toward discovery of crucial innate immune signaling regulators, yet model-derived predictions are almost completely unexplored. Here, we carried out systematic experimental validation of candidate regulators predicted by a transcriptional association network model of influenza virus-infected cells. We identified dozens of novel innate immune signaling regulators with potent effects on the replication of influenza and other viruses, and importantly, we established the biological relevance of a validated regulator in vivo . Collectively, these findings aid in clarifying mechanisms of influenza virus pathogenicity and might lead to innovative approaches for treating influenza virus disease. Similar data-driven modeling strategies may be applicable for the study of other pathogen systems or immunological disorders.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".