Bibliographic record
Abstract
Genome-wide monitoring of gene expression using DNA microarrays represents one of the latest breakthroughs in experimental molecular biology and provides unprecedented opportunity to explore the biological processes underlying human diseases by providing a comprehensive survey of a cell's transcriptional landscape. In the cancer field, this revolutionary technology allows the simultaneous assessment of the transcription of tens of thousands of genes, and of their relative expression between normal cells and malignant cells. As microarray analysis emerges from its infancy, there is widespread hope that microarrays will significantly impact on our ability to explore the genetic changes associated with cancer etiology and development, and ultimately lead to the discovery of new biomarkers for disease diagnosis and prognosis prediction, and of new therapeutic tools. This review provides an overview of microarray technology, specifically in the context of cancer research and describes some of its recent applications to the study of cancer. In addition, the challenges of translating microarray findings into molecular cancer diagnosis and prognosis tools, with the potential of altering clinical practice through individualized cancer care and ultimately of contributing to the battle against cancer, are discussed.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".