A critical path to producing high quality, reproducible data from quantitative western blot experiments
Bibliographic record
Abstract
Western blotting experiments were initially performed to detect a target protein in a complex biological sample and more recently, to measure relative protein abundance. Chemiluminescence coupled with film-based detection was traditionally the gold standard for western blotting but accurate and reproducible quantification has been a major challenge from this methodology. The development of sensitive, camera-based detection technologies coupled with an updated technical approach permits the production of reproducible, quantitative data. Fluorescence reagent and detection solutions are the latest innovation in western blotting but there remains questions and debate concerning their relative sensitivity and dynamic range versus chemiluminescence. A methodology to optimize and produce excellent, quantitative western blot results with rigorous data analysis from membranes probed with both fluorescent and chemiluminescent antibodies is described. The data reveal when and how to apply these detection methods to achieve reproducible data with a stepwise approach to data processing for quantitative analysis.
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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 | 0.058 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.019 |
| Insufficient payload (model declined to judge) | 0.007 | 0.010 |
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".