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AlphaGenome informed machine learning for pathogenicity prediction of germline mutations

Accurate interpretation of germline variants remains a central challenge in clinical genomics. Existing in silico pathogenicity predictors often provide incomplete coverage or restricted to missense variants from the coding region, leaving the variants from splice sites, -UTR, and intronic regions under-scored. We present AGVarPred, a gradient-boosted classifier designed to predict the…

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