Computational methods for identification of recurrent copy number alteration patterns by array CGH
Bibliographic record
Abstract
Recurrent DNA copy number alterations (CNA) are widely studied in diagnostic and cytogenetic cancer research. CNAs reveal locations that may alter gene dosage and thus expression of the genes contained within. Array comparative genomic hybridization has emerged as a popular high-throughput, genome-wide technique to interrogate tumor genomes for copy number alterations. When studying a group of tumors derived from a patient cohort, it is of great interest to detect the copy number alterations that are common across the population and thus assumed to be potential diagnostic markers and/or predictors of clinical outcome. In this paper, we review extant and available computational approaches for detecting such recurrent copy number alterations from array comparative genomic hybridization (aCGH) data. This is a challenging computational problem due to various sources of noise in the data that can obscure the recurrent copy number signals or induce false positives in their prediction. In this paper, we qualitatively evaluate methods designed to detect recurrent copy number alterations for aCGH data based on their analytical strengths and limitations, and discuss expected future directions in this important area of cancer research.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".