ArticleLeukemia2024
Real life evaluation of AlphaMissense predictions in hematological malignancies.
Article in Leukemia, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- A comprehensive map of missense trafficking variants in rhodopsin and their response to pharmacologic correction.Science advances · 2026Article
- AlphaMissense prediction for the evaluation of missense variants in the diagnostic setting of neuromuscular disorders.Journal of neuromuscular diseases · 2026Article
- Interpreting rare missense variants in ABCB4 using ACMG recommended in silico tools, bespoke prediction algorithm Vasor and AlphaMissense.Scientific reports · 2026Article
- Unveiling the influence of fastest nobel prize winner discovery: alphafold's algorithmic intelligence in medical sciences.Journal of molecular modeling · 2025Review
- Deep learning-based assessment of missense variants in theBMJ open ophthalmology · 2025Article
- Assessment of ability of AlphaMissense to identify variants affecting susceptibility to common disease.European journal of human genetics : EJHG · 2024Article
- The Systemic Genotype-Phenotype Characterization of PAX6-Related Eye Disease in 164 Chinese Families.Investigative ophthalmology & visual science · 2024Article
- Germline Variants in Sporadic Pituitary Adenomas.Journal of the Endocrine Society · 2024Article
- Evaluation of next-generation sequencing versus next-generation flow cytometry for minimal-residual-disease detection in Chinese patients with multiple myeloma.Discover oncology · 2024Article
- Comprehensive evaluation of AlphaMissense predictions by evidence quantification for variants of uncertain significance.Frontiers in genetics · 2024Article
- Deep Learning in Hematology: From Molecules to Patients.Clinical hematology international · 2024Review
- Correlations ofAlzheimer's & dementia (New York, N. Y.)Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-throughput sequencing plays a pivotal role in hematological malignancy diagnostics, but interpreting missense mutations remains challenging. In this study, we used the newly available AlphaMissense database to assess the efficacy of machine learning to predict missense mutation effects and its impact to improve our ability to interpret them. Based on the analysis of 2073 variants from 686 patients analyzed for clinical purpose, we confirmed the very high accuracy of AlphaMissense predictions in a large real-life data set of missense mutations (AUC of ROC curve 0.95), and provided a comprehensive analysis of the discrepancies between AlphaMissense predictions and state of the art clinical interpretation.
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Registered trials
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