Multiprobe Oligonucleotide Solution Hybridization for the Determination of Relative mRNA Levels
Bibliographic record
Abstract
The ability to measure steady-state mRNA levels is central to the analysis of neuronal gene expression and, therefore, finds application across a broad range of neuroscientific research endeavors including the investigation of spatial-, temporal-, drug-induced-, or activity-dependent-differential gene expression, The actual measure required, however, depends to a large extent upon the experimental paradigm. For example, one of the first questions often addressed following the cloning of new genes is that of the tissue distribution of its expression, for which the technique of northern blotting (Alwine et al., 1977) is widely used (for example, see Webb et al., 1993). More detailed analyses of spatial expression patterns can be performed using in situ hybridization histochemistry (for example see Glencorse et al., 1993; Webb et al., 1994), the subject of a separate review in this volume (Bateson, 1998). The study of differential gene expression through development has also predominantly relied upon the techniques of Northern blotting and in situ hybridization histochemistry (for example see MacLennan et al., 1991; Laurie et al., 1992). These two methodologies are relatively straightforward to perform and allow the investigator to readily identify a brain region and/or cell type, or a developmental time point, where the gene of interest displays “maximal” expression without the need for rigorous quantification of mRNA levels.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.036 | 0.024 |
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".