Building an Adverse Outcome Pathway network for COVID-19
Bibliographic record
Abstract
The COVID-19 pandemic generated large amounts of data on the disease pathogenesis leading to a need for organizing the vast knowledge in a succinct manner. Between April 2020 and February 2023, the CIAO consortium exploited the Adverse Outcome Pathway (AOP) framework to comprehensively gather and systematically organize published scientific literature on COVID-19 pathology. The project considered 24 pathways relevant for COVID-19 by identifying essential key events (KEs) leading to 19 adverse outcomes observed in patients. While an individual AOP defines causally linked perturbed KEs towards an outcome, building an AOP network visually reflect the interrelatedness of the various pathways and outcomes. In this study, 17 of those COVID-19 AOPs were selected based on quality criteria to computationally derive an AOP network. This primary network highlighted the need to consider tissue specificity and helped to identify missing or redundant elements which were then manually implemented in the final network. Such a network enabled visualization of the complex interactions of the KEs leading to the various outcomes of the multifaceted COVID-19 and confirmed the central role of the inflammatory response in the disease. In addition, this study disclosed the importance of terminology harmonization and of tissue/organ specificity for network building. Furthermore the unequal completeness and quality of information contained in the AOPs highlighted the need for tighter implementation of the FAIR principles to improve AOP findability, accessibility, interoperability and re-usability. Finally, the study underlined that describing KEs specific to SARS-CoV-2 replication and discriminating physiological from pathological inflammation is necessary but requires adaptations to the framework. Hence, based on the challenges encountered, we proposed recommendations relevant for ongoing and future AOP-aligned consortia aiming to build computationally biologically meaningful AOP networks in the context of, but not limited to, viral diseases.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".