Natalie A. Twine

The paper introduces synerOmics, a machine learning framework to identify synergistic protein interactions, finding ATL3 as a biomarker for lapatinib resistance in breast cancer.

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.

BitEpi efficiently identifies higher-order epistatic interactions among SNVs in genetic data, outperforming existing methods in speed and accuracy.

TRIBES detects genomic relatedness up to 7th degree relatives and identifies disease susceptibility loci using a single pipeline.