The Deep Genome Project
Bibliographic record
Abstract
In vivo research is critical to the functional dissection of \nmulti-organ systems and whole organism physiology, and \nthe laboratory mouse remains a quintessential animal model \nfor studying mammalian, especially human, pathobiology. \nEnabled by technological innovations in genome sequencing, \nmutagenesis and genome editing, phenotype analyses, and \nbioinformatics, in vivo analysis of gene function and dysfunction \nin the mouse has delivered new understanding of the \nmechanisms of disease and accelerated medical advances. \nHowever, many significant hurdles have limited the elucidation \nof mechanisms underlying both rare and complex, \nmultifactorial diseases, leaving significant gaps in our scientific \nknowledge. Future progress in developing a functionally \nannotated genome map depends upon studies in model organisms, \nnot least the mouse. Further, recent advances in \ngenetic manipulation and in vivo, in vitro, and in silico phenotyping \ntechnologies in the mouse make annotation of the \nvast majority of functional elements within the mammalian \ngenome feasible. The implementation of a Deep Genome \nProject—to deliver the functional biological annotation of all human orthologous genomic elements in mice—is an essential \nand executable strategy to transform our understanding \nof genetic and genomic variation in human health and disease \nthat will catalyze delivery of the promised benefits of \ngenomic 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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".