Bibliographic record
Abstract
The use of DNA microarrays for the analysis of complex biological samples is becoming a mainstream part of biomedical research. One of the most commonly used methods compares the relative abundance of mRNA in two different samples by probing a single DNA microarray simultaneously. The simplicity of this concept sometimes masks the complexity of capturing and processing microarray data. On the basis of the analysis of many of our microarray experiments, we identified the major causes of distortion of the microarray data and the sources of noise. In this study, we provide a systematic statistical approach for extraction of true expression ratios from raw microarray data, which we describe as an unfolding process. The results of this analysis are presented in the form of a model describing the relationship between the measured fluorescent intensities and the concentrations of mRNA transcripts. We developed and tested several algorithms for inference of the model parameters for the microarray data. Special emphasis is given to the statistical robustness of these algorithms, in particular resistance to outliers. We also provide methods for measurement of noise and reproducibility of the microarray experiments.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".