AnnotateMissense: a genome-wide annotation and benchmarking framework for missense pathogenicity prediction
arXiv:2605.24520v1 Announce Type: cross Abstract: Missense variant interpretation remains challenging because pathogenicity depends on heterogeneous evidence from population frequency, evolutionary conservation, transcript context, amino acid substitution severity, prior pathogenicity predictors and protein-language-model-derived features. We present AnnotateMissense, a scalable annotation, benchmarking and genome-wide prediction framework for missense variant interpretation. AnnotateMissense integrates hg38 missense variants derived from dbNSFP v5.1 with ANNOVAR annotations, dbNSFP transcript
The proliferation of advanced AI models and the increasing availability of genomic data are enabling more sophisticated approaches to genetic variant interpretation.
Improved pathogenicity prediction for missense variants is crucial for advancing personalized medicine, drug discovery, and understanding disease mechanisms.
The ability to accurately annotate and benchmark missense variants genome-wide could significantly accelerate genetic research and clinical diagnostics.
- · Genomic sequencing companies
- · Pharmaceutical R&D
- · Diagnostic labs
- · AI-driven biotech
- · Traditional, manual annotation methods
- · Less efficient prediction tools
More accurate and faster identification of disease-causing genetic mutations.
Accelerated development of targeted therapies and improved patient outcomes for genetic diseases.
Potential for a new era of proactive, preventative medicine based on comprehensive genomic risk assessment.
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Read at arXiv cs.LG