Vikas Rao Pejaver

The paper explores patient and clinician perspectives on identifying and managing the underlying causes of severe hypercholesterolemia through genomic insights.

This paper introduces a framework to integrate social determinants of health into disease risk models using biobank survey data, showing improved prediction beyond genetic factors.

This study calibrates in-frame indel variant effect predictors for clinical classification, showing they reach multiple evidence levels but perform worse than missense predictors.

This paper improves variant effect predictors by calibrating them gene- and domain-aware, increasing evidence assignment and accuracy for variant classification.

The paper develops a scalable workflow to reclassify variants of uncertain significance using experimental and predictive evidence, resolving 75% of VUS in 40 genes.

This study identifies diagnostic delay in mitochondrial disease as primarily due to missed recognition of canonical features, not testing limitations, and suggests AI/NLP tools for earlier detection.

The paper uses population biobank data to identify and calibrate evidence of variant pathogenicity for 41 genes, finding that 2.6% of participants have rare variants with moderate or stronger evidence of pathogenicity.

This paper introduces ExCALIBR, a method to calibrate variant scores from functional assays, improving pathogenicity prediction and reducing variants of uncertain significance.

The paper calibrates three new computational tools for clinical variant classification, finding they provide evidence strength comparable to previously recommended tools.

The paper evaluates variant effect predictions for ARSA gene variants, finding that a bootcamp-trained model outperformed others and that deep learning slightly improved performance over simpler methods.

The paper evaluates missense variant effect predictors using CAGI6 data, finding strong performance across settings and highlighting biases in some predictors.

The paper assesses rare missense variants in genome sequences using PP3/BP4 evidence strength classifications and finds few variants meet strong or moderate criteria.

The paper evaluates variant prioritization methods for rare disease diagnosis using data from the Rare Genomes Project, finding that top models identify causal variants in most solved families and uncover potential candidates in unsolved ones.

The paper calibrates computational tools for missense variant pathogenicity classification and provides evidence-based revisions to PP3/BP4 ACMG/AMP criteria.

MutPred2 identifies pathogenic amino acid variants, predicts their molecular mechanisms, and validates their functional impact in neurodevelopmental disorders.