Módszerek összehasonlítása

	Gépi tanulással támogatott másolatszám-variáció elemzés	Copy Number Variation Analysis
Tudományterület	Bioinformatika	Bioinformatika
Módszercsalád	Process / pipeline	Process / pipeline
Keletkezés éve	2010s–present	1998–2006
Megalkotó	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)
Típus	Supervised/unsupervised machine learning pipeline for genomic structural variant detection	Genomic structural variant detection pipeline
Alapmű	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.	Redon, R., Ishikawa, S., Fitch, K. R., et al. (2006). Global variation in copy number in the human genome. Nature, 444(7118), 444–454.
Alternatív nevek	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
Kapcsolódó	6	6
Összefoglaló	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.