Affymetrix Oligonucleotide Analysis of Gene Expression in the Injured Heart
Bibliographic record
Abstract
Microarrays have helped researchers gain much insight into gene expression profiles in the context of many diseases including those in the injured heart. Our genomic investigations have been focused on elucidation of host gene responses to enterovirus infection. We have gained valuable technical expertise in using Affymetrix oligonucleotide arrays, also known as GeneChips, and cDNA spotted arrays to probe differential gene expression in both cultured cells and in heart tissue. Here, we provide a technique-focused supplement to the Affymetrix GeneChip Expression Analysis Manual for sample preparation, processing, and array hybridization. We provide expanded explanations to highlight important points within the existing protocol and offer variations to standard procedures when appropriate. For investigators using myocardial tissues for microarray experiments, we further address the necessity of and methods for in situ flushing of the vasculature, tissue homogenization, and considerations for limits of expression detection in rare cells. It is our intention to provide useful technical information, based on our experience, to assist those researchers using Affymetrix GeneChips in their own genomic research.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.007 |
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".