The developmental transcriptome of the human brain
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE OF REVIEW: Recent characterizations of the transcriptome of the developing human brain by several groups have generated comprehensive datasets on coding and noncoding RNAs that will be instrumental for illuminating the underlying biology of complex neurodevelopmental disorders. This review summarizes recent studies successfully utilizing these data to increase our understanding of the molecular mechanisms of pathogenesis. RECENT FINDINGS: Several approaches have successfully integrated developmental transcriptome data with gene discovery to generate testable hypotheses about when and where in the developing human brain disease-associated genes converge. Specifically, these include the projection neurons in the prefrontal and primary motor--somatosensory cortex during mid-fetal development in autism spectrum disorder and the frontal cortex during fetal development in schizophrenia. SUMMARY: Developmental transcriptome data is a key to interpreting disease-associated mutations and transcriptional changes. Novel approaches integrating the spatial and temporal dimensions of these data have increased our understanding of when and where disease occurs. Refinement of spatial and temporal properties and expanding these findings to other neurodevelopmental disorders will provide critical insights for understanding disease biology.
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.
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 | 0.000 | 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 it