Ocular Phenotyping of Knockout Mice Identifies Genes Associated With Late Adult Retinal Phenotypes
Bibliographic record
Abstract
Purpose: Analyze phenotypic data from knockout mice with late-adult retinal pathologic phenotypes to identify genes associated with development of adult-onset retinal diseases. Methods: The International Mouse Phenotyping Consortium (IMPC) database was queried for genes associated with abnormal retinal phenotypes in the late-adult knockout mouse pipeline (49-80 weeks postnatal age). We identified human orthologs and performed protein-protein analysis and biological pathways analysis with known inherited retinal disease (IRD) and age-related macular degeneration (AMD) genes using Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), PLatform for Analysis of single cell Eye in a Disk (PLAE), Protein Analysis Through Evolutionary Relationships (PANTHER), and Kyoto Encyclopedia of Genes and Genomes (KEGG). Results: Screening of 587 late-adult mouse genes yielded 12 with abnormal retinal phenotypes, which corresponded to 20 human orthologs. Three of the 12 mouse genes and two of the 20 human orthologs were previously implicated in retinal pathology or physiology in a literature review. Although all of the genes demonstrated retinal pathology when deleted from the mouse genome, most do not have established roles in human retinal disease. Furthermore, human protein-protein analysis and biological pathway analysis yielded only a few relationships between the candidate gene list and that of known IRD and AMD genes, suggesting they may represent novel retinal functions. Conclusions: We identified 12 mouse genes with significant late-adult abnormal retinal pathology, eight of which have not been previously implicated in either mouse or human retinal physiology or pathology. These serve as novel retinal disease gene candidates for late-onset retinal disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".