The Deep Genome Project
Bibliographic record
Abstract
In vivo research is critical to the functional dissection of multi-organ systems and whole organism physiology, and the laboratory mouse remains a quintessential animal model for studying mammalian, especially human, pathobiology. Enabled by technological innovations in genome sequencing, mutagenesis and genome editing, phenotype analyses, and bioinformatics, in vivo analysis of gene function and dysfunction in the mouse has delivered new understanding of the mechanisms of disease and accelerated medical advances. However, many significant hurdles have limited the elucidation of mechanisms underlying both rare and complex, multifactorial diseases, leaving significant gaps in our scientific knowledge. Future progress in developing a functionally annotated genome map depends upon studies in model organisms, not least the mouse. Further, recent advances in genetic manipulation and in vivo, in vitro, and in silico phenotyping technologies in the mouse make annotation of the vast majority of functional elements within the mammalian genome feasible. The implementation of a Deep Genome Project—to deliver the functional biological annotation of all human orthologous genomic elements in mice—is an essential and executable strategy to transform our understanding of genetic and genomic variation in human health and disease that will catalyze delivery of the promised benefits of genomic medicine to children and adults around the world.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.062 | 0.047 |
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".