Mark R. Hurle

Network and pathway expansion of genetic disease associations identifies successful drug targets

This study uses genetic associations from GWAS and network propagation to identify drug targets enriched for clinical success.

Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases

This paper uses Mendelian randomization and colocalization to identify causal relationships between plasma proteins and disease phenotypes, finding 111 putatively causal effects.

Identification of putative effector genes across the GWAS Catalog using molecular quantitative trait loci from 68 tissues and cell types

The paper introduces PICCOLO, a method to identify colocalized genes between GWAS and xQTLs, finding 30% of associations have at least one colocalized gene-xQTL.

Pathway analysis of GWAS loci identifies novel drug targets and repurposing opportunities

This study uses pathway analysis of GWAS loci to identify novel drug targets and repurposing opportunities for 182 diseases, finding that 30% of diseases have significantly more targets in these pathways than expected by chance.

Predicting clinically promising therapeutic hypotheses using tensor factorization

The paper uses tensor factorization to predict successful therapeutic target-indication pairs by analyzing clinical trial data and comparing performance with other machine learning models.

Systematic interrogation of diverse Omic data reveals interpretable, robust, and generalizable transcriptomic features of clinically successful therapeutic targets

The paper identifies transcriptomic features of therapeutic targets that predict clinical success using Omic data and machine learning.