Akash Kamandula

Evaluation of enzyme activity predictions for variants of unknown significance in Arylsulfatase A

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.

Critical assessment of missense variant effect predictors on disease-relevant variant data

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

Critical assessment of variant prioritization methods for rare disease diagnosis within the Rare Genomes Project

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.

Predicting the impact of rare variants on RNA splicing in CAGI6

The paper evaluates splicing prediction methods on 56 clinically validated variants, finding some methods match or exceed SpliceAI's performance but highlighting ongoing challenges in accuracy.