New framework AnnotateMissense predicts missense variant pathogenicity

Researchers have developed AnnotateMissense, a new framework for predicting the pathogenicity of missense genetic variants. This system integrates a wide array of data, including population frequency, evolutionary conservation, and features derived from protein language models like AlphaMissense and ESM. Benchmarking against over 130,000 ClinVar-labeled variants showed that an XGBoost model using 303 features achieved a high performance with an MCC of 0.9411. The framework was then applied to over 90 million variants to generate pathogenicity scores. AI

IMPACT Provides a new tool for interpreting genetic variants, potentially accelerating research in genomics and personalized medicine.

RANK_REASON The cluster contains a research paper detailing a new framework and model for genetic variant interpretation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• AlphaMissense
• AnnotateMissense
• ANNOVAR
• ClinVar
• XGBoost

• paper
• other

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →