Ultradense Array CGH and Discovery of Micro-Copy Number Alterations and Gene Fusions in the Cancer Genome
Bibliographic record
Abstract
The characterization of molecular alterations specific to cancer facilitates the discovery of predictive and prognostic biomarkers important to targeted therapeutics. Alterations critical to cancer therapeutics include copy number alterations (CNAs) such as gene amplifications and deletions as well as genomic rearrangements resulting in gene fusions. There are two genome-wide technologies used to detect CNAs: next generation sequencing (NGS) and dense microarray based comparative genomic hybridization, termed array CGH (aCGH). aCGH is a mature robust technology of lower cost and more accessible than NGS. This chapter describes the protocol steps and analysis required to obtain reliable aCGH results from clinical samples. Technical options and various necessary compromises related to the nature of clinical material are considered and the consequences of these choices for data analysis and interpretation are discussed. The chapter includes brief description of the data analysis, even though analysis is often performed by bioinformaticians. Today's cancer research requires collaboration of clinicians, molecular biologists, and mathematicians. Acquaintance with the basic principles related to the extraction of the data from arrays, its normalization and the algorithms available for analysis provides a baseline for mutual understanding and communication.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".