ArticleJournal of translational medicine2025
Application and clinical utility assessment of natural language processing-based software for copy-number variants interpretation.
Article in Journal of translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation.Global medical genetics · 2026Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
backgroundManual interpretation of copy-number variant (CNV) according to the guideline published by the American College of Medical Genetics and Genomics (ACMG) and the Clinical Genome Resources (ClinGen) in 2020 is labor-intensive and time-consuming. The application of natural language processing (NLP)-based software like CNVisi can reduce the burden of CNV interpretation, but its clinical utility needs to be further evaluated.
methodsWe firstly used 1000 CNVs which had been previously manually classified to assess the performance of CNVisi. To assess the clinical utility of CNVisi, we collected 5861 CNVs from 2443 next-generation sequencing (NGS)-based CNV sequencing (CNV-seq) samples. The CNVs were first classified by CNVisi and then reviewed by genetic experts. After removing duplicates, the remaining 3384 CNVs were used for assessment of classification consistency, and 154 CNVs that met the reporting rules were finally selected for further analysis.
resultsThe overall accuracy of CNVisi in distinguishing pCNVs (Pathogenic or Likely Pathogenic CNVs) was 97.7% (977/1000) in preliminary assessment of performance. And the accuracy of CNVisi in assessment of clinical utility was 99.6% (3370/3384). Among 154 CNVs that met clinical reporting rules, 23 CNVs were classified with disagreement between CNVisi and genetic experts. The inconsistency in classification is mainly caused by the overlap between CNV and low-penetrance regions, and the difference in scoring of evidence related to the literature. According to the reporting rules, total CNVs were classified with a high consistency of 98.6% (5781/5861) between genetic experts and CNVisi, and the CNV-seq results of 96.9% (2367/2443) samples could be accurately and efficiently interpreted by CNVisi. Furthermore, CNVisi was superior to previous tools for CNV interpretation and classification, and showed excellent clinical utility.
conclusionsApplying CNV interpretation software such as CNVisi with clinical utility can reduce the burden of genetic experts and improve the efficiency of CNV interpretation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.