Ava Khamseh

Targeted-learning GWAS of 1,268 UK Biobank ME/CFS cases found 176 FDR-significant variants; seven replicated in disjoint UK Biobank or All of Us cohorts.

The paper profiles the morphological effects of 2,565 human and 1,900 mouse miRNAs on 24 million cells across five cell lines, finding that 9% and 15% of miRNA mimics significantly alter cell morphology.

The paper identifies epistatic interactions among DNA variants affecting human traits by combining machine learning with experimentally verified transcription factor binding data.

This study conducts a genome-wide association study (GWAS) of ME/CFS, identifying eight genetic loci associated with the disease, suggesting involvement of immune and neurological processes.

This study identifies epithelial state transitions driven by inflammation that promote tumorigenesis, showing that targeting NOTCH signaling can prevent pre-neoplastic to neoplastic transition.

The paper identifies context-dependent gene essentiality in GBM using CRISPR screening and develops a platform to link biomarkers to therapeutic targets.

Stator identifies cell types, subtypes, and states in single-cell data by analyzing higher-order gene expression dependencies, revealing cryptic states and subtypes not captured by proximity-based clustering.

This paper uses Mendelian randomisation to identify potential drug targets for cardiovascular disease by analysing protein levels in plasma and PBMCs from large cohorts.

TarGene is a statistical workflow that estimates effect sizes and epistatic interactions without parametric assumptions, validated on UK Biobank data and showing genetic non-linearity and epistasis.