Comparing Sequence-Based and Literature-Based Pathogenicity Scoring Methods for Human Variants.
PolyPhen-2
SIFT
Text-mining
Variant pathogenicity
Journal
Studies in health technology and informatics
ISSN: 1879-8365
Titre abrégé: Stud Health Technol Inform
Pays: Netherlands
ID NLM: 9214582
Informations de publication
Date de publication:
22 Aug 2024
22 Aug 2024
Historique:
medline:
23
8
2024
pubmed:
23
8
2024
entrez:
23
8
2024
Statut:
ppublish
Résumé
Assessing the pathogenicity of genetic variants is a critical aspect of genomic medicine and precision healthcare. Over the last decades, the identification of genetic variants and their characterization has become simpler (advent of high-throughput sequencing technologies, analysis, and visualization support tools, etc.). However, the quality of assessments to distinguish benign from pathogenic variants is critical to inform clinical decision-making and improve patient outcomes. In this article, we investigate the relationships using correlation tests between the characterization of genetic variants in the literature and their pathogenicity scores computed by two state-of-the-art assessment tools (SIFT and PolyPhen-2).
Identifiants
pubmed: 39176534
pii: SHTI240747
doi: 10.3233/SHTI240747
doi:
Types de publication
Journal Article
Comparative Study
Langues
eng
Sous-ensembles de citation
IM