Current Applications of Microarrays in Head and Neck Cancer Research
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: The objective was to introduce microarray technology and its applications in cancer research to the head and neck clinician. STUDY DESIGN: Literature review combined with methodology and examples from the authors' experiences with microarray analysis of tumors of the head and neck. METHODS: Search of literature and the authors' experience was made for technical details, alternative methods of data analysis, available bioinformatics tools, and applications of microarrays in cancer research. RESULTS: Microarrays allow the simultaneous analysis of the expression of thousands of genes. The use of a well-developed microarray study design leads to informative results. There are various bioinformatics resources widely available to aid in the analysis of microarray data. However, there is not yet a gold standard for analysis because this methodology is still evolving. CONCLUSION: Microarray studies may allow researchers to identify genetic changes relevant to diagnosis and prognosis in patients with head and neck cancer. Although still relatively new, this powerful methodology has immense potential to aid in understanding of the genetic changes that are important in head and neck cancer.
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 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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".