Bloomsbury report on mouse embryo phenotyping: recommendations from the IMPC workshop on embryonic lethal screening
Bibliographic record
Abstract
Identifying genes that are important for embryo development is a crucial first step towards understanding their many functions in driving the ordered growth, differentiation and organogenesis of embryos. It can also shed light on the origins of developmental disease and congenital abnormalities. Current international efforts to examine gene function in the mouse provide a unique opportunity to pinpoint genes that are involved in embryogenesis, owing to the emergence of embryonic lethal knockout mutants. Through internationally coordinated efforts, the International Knockout Mouse Consortium (IKMC) has generated a public resource of mouse knockout strains and, in April 2012, the International Mouse Phenotyping Consortium (IMPC), supported by the EU InfraCoMP programme, convened a workshop to discuss developing a phenotyping pipeline for the investigation of embryonic lethal knockout lines. This workshop brought together over 100 scientists, from 13 countries, who are working in the academic and commercial research sectors, including experts and opinion leaders in the fields of embryology, animal imaging, data capture, quality control and annotation, high-throughput mouse production, phenotyping, and reporter gene analysis. This article summarises the outcome of the workshop, including (1) the vital scientific importance of phenotyping embryonic lethal mouse strains for basic and translational research; (2) a common framework to harmonise international efforts within this context; (3) the types of phenotyping that are likely to be most appropriate for systematic use, with a focus on 3D embryo imaging; (4) the importance of centralising data in a standardised form to facilitate data mining; and (5) the development of online tools to allow open access to and dissemination of the phenotyping data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.075 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.010 | 0.013 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.025 | 0.028 |
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".