Analysis of gene expression profiles in pancreatic carcinoma by using cDNA microarray.
Bibliographic record
Abstract
OBJECTIVES: To survey the gene expression profiles in pancreatic carcinoma by using cDNA microarray and detect target genes for further study. METHODS: Three mixed samples from 2 cases of normal pancreatic tissue and 4 cases of moderate-differentiated pancreatic carcinoma were studied by means of cDNA microarray consisting of 18 000 genes. RESULTS: 1484 and 1353 different expressed genes were observed in two cancer samples respectively. We identified 455 genes altered with the same tendency in both samples, including 102 up-regulated and 353 down-regulated genes. There were 274 known genes and 181 unknown genes; 27.8% and 52.0% genes respectively had an expression level in cancer that was 2-fold higher or lower than that in normal samples. Tumor suppressor genes, growth factors and receptor genes, signal conduction genes, transcription factor genes were identified. CONCLUSIONS: cDNA microarray is an efficient and high-throughout method to investigate gene expression profiles in pancreatic carcinoma. MBD1, EDG1 and gene hypermethylation mechanism would play an important role in the pathogenesis of pancreatic carcinoma.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".