Expression profiling by microarrays in colorectal cancer (Review)
Bibliographic record
Abstract
Genome-wide gene profiling studies using microarrays have the potential to improve diagnosis and treatment of human cancers. Microarrays have identified many genes that are deregulated in colorectal cancer compared to normal tissue. Groups of genes that are predictive of tumor stage or presence of metastases, hence putatively associated with cancer progression have also been revealed. Microarray studies have identified genes whose expression are impacted by chemotherapies for colorectal cancer, thus could potentially be used to predict response to treatments. Unique gene expression profiles have also been used to classify metastases of uncertain origin. The wide application of microarrays generates exciting prospects in translational research. However, to date overlaps of candidate gene lists associated with specific clinical/biological phenotypes remain disturbingly poor between studies. Overfitting, bias, reporting of only the best results, and fidelity of probe annotations could present limitations for the interpretation of results shown in microarray publications. Making raw data from these microarray experiments publicly available for analysis by other investigators using different analytical algorithms or for in silico studies may facilitate the most thorough mining of data from these expensive studies. Validations of the results using other more precise techniques and at the biological level represent critical follow-up goals for microarray studies.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.004 |
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".