Nathan Linial

This study uses GWAS and PWAS on UK Biobank data to identify 137 cancer risk loci, highlighting recessive effects and cancer-type specificity.

PWAS detects protein-coding genes associated with phenotypes by analyzing proteomic context and using machine learning, showing improved detection of causal genes and new associations.

The paper uses a stochastic framework to analyze gene sensitivity to miRNA regulation in HeLa cells, identifying gene sets particularly sensitive to miRNA overexpression.

The paper uses a probabilistic framework to study miRNA regulation, finding that translation machinery is insensitive to miRNA manipulations across cell types.

The paper introduces a machine learning framework to identify cancer driver genes by analyzing the functional impact of somatic mutations, finding 593 genes with significant harmful mutation bias.

This study identifies cancer types using lowly expressed microRNAs from TCGA data, achieving 91% recall after data discretization.