Application of Gene Microarrays in the Study of Prostate Cancer
Bibliographic record
Abstract
Gene macro- and microarrays have become an increasingly popular tool to investigate gene expression patterns by simultaneously analyzing the expression of thousands of genes in a single experiment. Through careful array design and appropriate analytical tools, one can relate gene expression patterns across large series of data to determine clusters of genes that are co-ordinately regulated as well as correlate patterns to particular disease states or experimental conditions. This approach has been applied to derive relationships between clinical parameters of certain cancers with gene profiles, which potentially may be used as prognostication tools or for identification of therapeutic targets. Although still in its infancy as a clinical laboratory tool, there are several recent reports that illustrate the power of gene microarrays for differential cancer diagnosis. For example, analysis of mRNA from diffuse large B-cell lymphomas using gene microarrays with approx 18,000 cDNA clones revealed two distinct gene expression patterns that were indicative of newly uncovered cancer subtypes and that were predictive of disease survival (1). Similarly, there is some preliminary evidence from gene microarray analyses to suggest that gene expression patterns in human breast cancers form two distinctive clusters that correlate with cell proliferation rates and activation of the interferon signal transduction pathway (2), although no direct clinical or pathological connections were noted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".