2023
DOI: 10.1038/s41598-023-37352-1
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Combination of expert guidelines-based and machine learning-based approaches leads to superior accuracy of automated prediction of clinical effect of copy number variations

Abstract: Clinical interpretation of copy number variants (CNVs) is a complex process that requires skilled clinical professionals. General recommendations have been recently released to guide the CNV interpretation based on predefined criteria to uniform the decision process. Several semiautomatic computational methods have been proposed to recommend appropriate choices, relieving clinicians of tedious searching in vast genomic databases. We have developed and evaluated such a tool called MarCNV and tested it on CNV re… Show more

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“…The use of AI has drastically increased in clinical genomics. It has been applied in a wide range of conditions and approaches, such as patient photography analysis (facial analysis for disease identification, radiologic studies, microscopy data) [ 164 ], cardiology predictions (hypertension incident, atrial fibrillation, aortic stenosis) [ 165 ] blood biomarkers (mantle cell lymphoma [ 166 ], anemia [ 167 ]), interpretation of copy number variants [ 168 ], or classification of non-coding variants [ 169 ]. Regarding variant pathogenicity predictions, AI has revolutionized the field, providing advanced tools for accurate assessment.…”
Section: Ai-driven Enhancement Of Predictive Models In Bioinformaticsmentioning
confidence: 99%
“…The use of AI has drastically increased in clinical genomics. It has been applied in a wide range of conditions and approaches, such as patient photography analysis (facial analysis for disease identification, radiologic studies, microscopy data) [ 164 ], cardiology predictions (hypertension incident, atrial fibrillation, aortic stenosis) [ 165 ] blood biomarkers (mantle cell lymphoma [ 166 ], anemia [ 167 ]), interpretation of copy number variants [ 168 ], or classification of non-coding variants [ 169 ]. Regarding variant pathogenicity predictions, AI has revolutionized the field, providing advanced tools for accurate assessment.…”
Section: Ai-driven Enhancement Of Predictive Models In Bioinformaticsmentioning
confidence: 99%