Expression profiling by microarrays in colorectal cancer (Review)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 it