Functional classification of interferon-stimulated genes identified using microarrays
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it