Differential Display of Eukaryotic mRNA
Bibliographic record
Abstract
Arbitrarily primed PCR is a method that was initially developed to analyze and compare genome complexity ( 1 , 2 ). By using arbitrary primers, an array of stochastic sequences can be amplified using the polymerase chain reaction (PCR) and resolved by denaturing acrylamide gels, generating a characteristic pattern or fingerprint. This technique can be readily applied for the analysis of differentially expressed genes in two different cell types by simply converting the mRNA into cDNA ( 3 , 4 ). A different fingerprint pattern will then reflect differential gene expression between the cell types analyzed. The number and intensity of the fingerprints depend largely on two parameters: the abundance of the original RNAs, and how well the primers match the primary sequence so that efficient amplification can take place. Taking this into consideration, the method appears to be more suitable for the comparison of cell types where a large number of differentially expressed genes are anticipated. A major advantage of this method when compared to other strategies used for the isolation of differentially expressed genes is that a very small amount of RNA is required. This makes the method very suitable for embryological studies, where sometimes the quantity of tissue that can be collected is limiting. 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".