RNA interference in mammals: behind the screen
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
The discovery of RNA interference (RNAi) and the development of technologies exploiting its biology have enabled scientists to rapidly examine the consequences of depleting a particular gene product in a cell or an animal. The availability of genome-wide RNAi libraries targeting the mouse and human genomes has made it possible to carry out large scale, phenotype-based screens, which have yielded seminal information on diverse cellular processes ranging from virology to cancer biology. Today, several strategies are available to perform RNAi screens, each with their own technical and monetary considerations. Special care and budgeting must be taken into account during the design of these screens in order to obtain reliable results. In this review, we discuss a number of critical aspects to consider when planning an effective RNAi screening strategy, including selecting the right biological system, designing an appropriate selection scheme, optimizing technical aspects of the screen, and validating and verifying the hits. Similar to an artistic production, what happens behind the screen has a direct impact on its success.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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