What have we learned from the embryonic transcriptome?
Bibliographic record
Abstract
During the last decade, transcriptome profiling has emerged as an efficient approach to describe and study cellular functions.The potential to survey transcript abundance for all genes offers promise to shed light on mammalian early embryogenesis.Furthermore, the report of aberrant phenotypes following the application of reproductive technologies also fueled the need to understand how embryos react, cope and adapt to their surrounding microenvironment.So far, the atypical nature of early blastomeres and the drastic transitions through which embryogenesis progresses posed and still pose numerous technical challenges such as to correctly interpret the natural fluctuation in total RNA and proteins contents.Although tedious, these technical considerations are important for data soundness and interpretation.In this review, we examine a number of transcriptomic surveys performed on blastocysts and demonstrate that several consistent observations have transpired that alter the conceptual issues regarding the definition of embryonic normalcy.Moreover, the need to complement the study of gene expression with profiling epigenomic marks is opening new perspectives that will also be discussed.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".