Letitia M.F. Sng

The paper evaluates AlphaGenome's cross-species performance on mouse data, showing varying accuracy for different functional tasks and introduces a confidence scoring method for AI predictions.

The paper identifies an epistatic interaction between RBMS3 and CDKN2B-AS1 in CAD risk using VariantSpark and BitEpi, with validation in UK Biobank and All of Us.

The paper fine maps CAD loci in UK Biobank whole genome sequencing data using PolyFun and ML tools, showing improved sensitivity over traditional methods and demonstrating cost and speed benefits of cloud-based analysis.

This study identifies novel Alzheimer's disease genes and epistasis using machine learning GWAS tools, capturing more phenotypic variance and discovering new loci with epistatic interactions.

RFlocalfdr thresholds variable importance from Random Forests using an empirical Bayes approach, achieving high accuracy and speed for high-dimensional datasets.