InnateDB & Cerebral: user‐friendly tools for the systems‐level analysis of innate immunity
Bibliographic record
Abstract
InnateDB is a publicly available knowledgebase of genes, proteins, interactions, and pathways involved in innate immunity. It integrates known interactions and pathways from public databases and manually curated data into a centralized resource, which includes data on >100,000 human and mouse interactions, cross‐references to innate immunity relevant pathways, and detailed annotation from a variety of sources. Building on this data we provide several bioinformatics tools to facilitate systems‐level investigations of the innate immune response. These include the ability to upload expression datasets, which can be integrated with network/pathway data to investigate changes in gene expression in a network of interest. This data can be investigated using our network visualization tool, Cerebral, a Cytoscape plugin which allows the generation of intuitive pathway and cell localization‐oriented views of interaction data. Cerebral allows the overlay of expression data from multiple experiments on top of interaction network data from InnateDB. We also provide orthology predictions for human, mouse & bovine genes to facilitate the construction of orthologous networks in different species. InnateDB is funded by Genome Canada, the Foundation for the National Institutes of Health, & the Canadian Institutes of Health Research under the Grand Challenges in Global Health Initiative.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".