Integrated Modeling of GC-Content, Mappability, Tumor Impurity and Aneuploidy for Accurate Detection of Genomic Aberrations
Bibliographic record
Abstract
Next-generation sequencing has been widely used in cancer-focused studies for comprehensive landscape of tumor genomes. Detection of genomic aberrations is one of the focal points in this area. Analysis of tumor sequencing data is usually complicated by several critical issues, such as GC-content bias, mappability bias, tumor impurity, and aneuploidy. Efficient computational methods are still in great demand for comprehensively addressing these issues. We introduce GPHMM-SEQ, a novel algorithm for inferring tumor impurity and ploidy as well as detecting copy number alterations and loss of heterozygosity from paired tumor-normal samples. Read depth signals derived from sequencing data are analyzed using a novel hidden Markov model that employs integrated representation of GC-content bias, mappability bias, tumor impurity, and aneuploidy. The evaluation on simulated and real tumor sequencing data demonstrates GPHMM-SEQ has the superior performance compared to existing methods.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".