Using ascidian embryos to study the evolution of developmental gene regulatory networks
Bibliographic record
Abstract
Ascidians are ideally positioned taxonomically at the base of the chordate tree to provide a point of comparison for developmental regulatory mechanisms that operate among protostomes, non-chordate deuterostomes, invertebrate chordates, and vertebrates. In this review, we propose a model for the gene regulatory network that gives rise to the ascidian notochord. The purpose of this model is not to clarify all of the interactions between molecules of this network, but to provide a working schematic of the regulatory architecture that leads to the specification of endoderm and the patterning of mesoderm in ascidian embryos. We describe a series of approaches, both computational and biological, that are currently being used, or are in development, for the study of ascidian embryo gene regulatory networks. It is our belief that the tools now available to ascidian biologists, in combination with a streamlined mode of development and small genome size, will allow for more rapid dissection of developmental gene regulatory networks than in more complex organisms such as vertebrates. It is our hope that the analysis of gene regulatory networks in ascidians can provide a basic template which will allow developmental biologists to superimpose the modifications and novelties that have arisen during deuterostome evolution.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".