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| Анализ на вариации в броя на копията с помощта на машинно обучение× | Анализ на вариациите в броя на копията× | |
|---|---|---|
| Област | Биоинформатика | Биоинформатика |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | 2010s–present | 1998–2006 |
| Създател≠ | Multiple groups; notable early ML-CNV tools include CNV-RF (Bellos et al., 2014) and CANOES (Backenroth et al., 2014) | Pinkel et al. (array CGH); Redon et al. (genome-wide CNV map) |
| Тип≠ | Supervised/unsupervised machine learning pipeline for genomic structural variant detection | Genomic structural variant detection pipeline |
| Основополагащ източник≠ | Aganezov, S., Goodwin, S., Sherman, R. M., Sedlazeck, F. J., Mehta, G., Rushbrook, S., ... & Schatz, M. C. (2020). Comprehensive analysis of structural variants in breast cancer genomes using single-molecule sequencing. Genome Research, 30(9), 1258-1273. link ↗ | Redon, R., Ishikawa, S., Fitch, K. R., et al. (2006). Global variation in copy number in the human genome. Nature, 444(7118), 444–454. DOI ↗ |
| Други названия | ML-CNV analysis, ML-based CNV calling, machine learning CNV detection, deep learning CNV analysis | CNV analysis, copy number variant detection, CNV calling, somatic copy number alteration analysis |
| Свързани | 6 | 6 |
| Резюме≠ | Machine learning-assisted CNV analysis applies supervised, unsupervised, or deep learning algorithms to detect genomic regions that are duplicated or deleted relative to a reference genome. Rather than relying on fixed statistical thresholds, ML models learn discriminative patterns from read-depth signals, allele frequencies, and other features, substantially improving sensitivity and specificity over classical tools — especially in noisy or low-coverage sequencing data. | Copy number variation (CNV) analysis is a genomic pipeline for detecting regions where individuals carry fewer or more copies of a DNA segment than the reference genome. CNVs span kilobases to megabases and are a major class of structural variation implicated in cancer, neurodevelopmental disorders, and population diversity. The pipeline typically processes SNP array intensities or read-depth signals from whole-genome sequencing, applies segmentation algorithms, calls gain and loss events, and annotates them against gene and clinical databases. |
| ScholarGateНабор от данни ↗ |
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