Genome-Wide Expression Profiling of Neurogenesis in Relation to Cell Cycle Exit
Bibliographic record
Abstract
Neurogenesis is the process by which new brain cells are produced either during development or in the adult brain. More specifically, it is “the proliferation of neuronal precursor cells to produce neurons.” Both definitions embody a key role for the cell cycle in the process particularly because the brain is an architecturally complex, multicompartmented tissue and the correct numbers of neurons (and glial cells) must be placed into each compartment. The process is made more complicated by the fact that neurons within each compartment are highly specialized, mandating that the new neurons also have the correct phenotype. Therefore, a mechanistic understanding of neurogenesis requires an understanding of several processes—control of the cell cycle to generate neurons in sufficient numbers, spatial mechanisms that ensure the correct number of cells in each compartment, the differentiation process that transforms a progenitor cell into a neuron, and an explanation of how so many neuronal subtypes can be readily created. Equally important is an understanding of the temporal coordination of these four processes, particularly regarding cell cycle exit. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".