The systematic identification of cytoskeletal genes required for Drosophila melanogaster muscle maintenance
Bibliographic record
Abstract
Animal muscles must maintain their function and structure while bearing substantial mechanical loads. How muscles withstand persistent mechanical strain is presently not well understood. Understanding the mechanisms by which tissues maintain their complex architecture is a key goal of cell biology. This dataset represents a systematic screen through the Drosophila melanogaster cytoskeleton to identify genes that are required to maintain tissue, specifically muscle, architecture. Using RNA interference (RNAi), we knocked down 238 genes in Drosophila and assayed for climbing ability with a robust behavioural assay. Here we present the summary of the screen and provide the complete results of the assays. We have uncovered a number of novel hits that would reward further study. The data are easy to use: the raw data are provided to allow researchers to perform their own analysis and analysed results are given indicating whether or not the genes are required for muscle maintenance. This dataset will allow other researchers to identify candidate genes for more detailed study and lead to better understanding of muscle maintenance.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".