Principle considerations for the use of transcriptomics in doping research
Bibliographic record
Abstract
Over the course of the past decade, technical progress has enabled scientists to investigate genome-wide RNA expression using microarray platforms. This transcriptomic approach represents a promising tool for the discovery of basic gene expression patterns and for identification of cellular signalling pathways under various conditions. Since doping substances have been shown to influence mRNA expression, it has been suggested that these changes can be detected by screening the blood transcriptome. In this review, we critically discuss the potential but also the pitfalls of this application as a tool in doping research. Transcriptomic approaches were considered to potentially provide researchers with a unique gene expression signature or with a specific biomarker for various physiological and pathophysiological conditions. Since transcriptomic approaches are considerably prone to biological and technical confounding factors that act on study subjects or samples, very strict guidelines for the use of transcriptomics in human study subjects have been developed. Typical field conditions associated with doping controls limit the feasibility of following these strict guidelines as there are too many variables counteracting a standardized procedure. After almost a decade of research using transcriptomic tools, it still remains a matter of future technological progress to identify the ultimate biomarker using technologies and/or methodologies that are sufficiently robust against typical biological and technical bias and that are valid in a court of law.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".