Functional classification of interferon-stimulated genes identified using microarrays
Bibliographic record
Abstract
Interferons (IFNs) are a family of multifunctional cytokines that activate transcription of subsets of genes. The gene products induced by IFNs are responsible for IFN antiviral, antiproliferative, and immunomodulatory properties. To obtain a more comprehensive list and a better understanding of the genes regulated by IFNs, we compiled data from many experiments, using two different microarray formats. The combined data sets identified >300 IFN-stimulated genes (ISGs). To provide new insight into IFN-induced cellular phenotypes, we assigned these ISGs to functional categories. The data are accessible on the World Wide Web at http://www.lerner.ccf.org/labs/williams/, including functional categories and individual genes listed in a searchable database. The entries are linked to GenBank and Unigene sequence information and other resources. The goal is to eventually compile a comprehensive list of all ISGs. Recognition of the functions of the ISGs and their specific roles in the biological effects of IFNs is leading to a greater appreciation of the many facets of these intriguing and essential cytokines. This review focuses on the functions of the ISGs identified by analyzing the microarray data and focuses particularly on new insights into the protein kinase RNA-regulated (PRKR) protein, which have been made possible with the availability of PRKR-null mice.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".