The Application of Single-Cell Omics Technologies in Neuroscientific Research
Bibliographic record
Abstract
With the deepening of neuroscientific research, traditional research methods have become difficult to meet the comprehensive analysis of the complexity and heterogeneity of the nervous system. The rise of single-cell omics technologies has brought new opportunities to neuroscientific research. This review summarizes the heterogeneity of nerve cells and the basic principles, advantages, and limitations of single-cell omics technologies. Through specific cases, it delves into the application of single-cell omics technologies in the study of neurons and synapses, the analysis of the pathogenesis of neurodegenerative diseases, as well as neural regeneration and repair research, and analyzes their contributions to neuroscience. In addition, this review also looks forward to the future development direction of single-cell omics technologies in neuroscientific research and discusses the current and future technical and ethical challenges and their solutions. This review aims to comprehensively sort out and evaluate the application of single-cell omics technologies in neuroscientific research, hoping to provide useful references and insights for researchers in related fields.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".