Benchmarking macaque brain gene expression for horizontal and vertical translation
Bibliographic record
Abstract
The spatial patterning of gene expression shapes cortical organization and function. The macaque is a fundamental model organism in neuroscience, but the translational potential of macaque gene expression rests on the assumption that it is a good proxy for patterns of corresponding proteins (vertical translation) and for patterns of orthologous human genes (horizontal translation). Here, we systematically benchmark regional gene expression in macaque cortex against (i) macaque cortical receptor density and in vivo and ex vivo microstructure and (ii) human cortical gene expression. We find moderate cortex-wide correspondence between macaque gene expression and protein density, which improves by considering layer-specific gene expression. Half of the examined genes exhibit significant correlation between macaque and human across the cortex. Interspecies correspondence of gene expression is greater in unimodal than in transmodal cortex, recapitulating evolutionary cortical expansion and gene-protein correspondence in the macaque. These results showcase the potential and limitations of macaque cortical transcriptomics for translational discovery within and across species.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".