Bioinformatic analysis of differentially expressed long non-coding RNAs in skeletal muscle following aerobic and resistance exercise
Bibliographic record
Abstract
Abstract Emerging evidence suggests that long non-coding RNA (lncRNA) molecules influence the adaptive response to exercise, but how lncRNA responses differ between endurance and resistance exercise modalities is poorly understood. The purpose of this study was to bioinformatically infer the expression of lncRNA in skeletal muscle following acute aerobic exercise (AE) and resistance exercise (RE). We downloaded publicly available RNA-seq data, performed a differential expression analysis, and compared lncRNA expression profiles between different exercise types (AE vs. RE) at three time points: baseline, 1 hour post-exercise, and 4 hours post-exercise. We observed distinct lncRNA profiles between acute AE and RE at different time points, suggesting that lncRNA perform different roles in controlling the response to different exercise modalities in skeletal muscle. Future studies should investigate the specific roles of these lncRNAs in the response to acute exercise in skeletal muscle.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".