Ellen M. McDonagh

This paper introduces Karenina, a framework that evaluates biomedical AI using expert knowledge across multiple dimensions beyond traditional scoring.

VIDRA models genetic dose-response relationships to predict drug mechanisms, therapeutic potential, and adverse events using phenotypically associated variants.

This paper analyzes 100,526 GWAS to identify pleiotropic genes and their therapeutic implications, finding that highly pleiotropic genes are associated with safety liabilities and that combining PAV support with intermediate pleiotropy improves therapeutic success.

This study shows that plasma protein quantitative trait loci (pQTL) support therapeutic hypotheses more effectively than other genetic evidence, increasing the likelihood of drug development success.

This study systematically analyzes chemical probe literature to identify target-disease associations, revealing their utility in early therapeutic discovery and highlighting opportunities for repurposing.

The paper introduces a framework for extracting biological evidence from literature to aid drug discovery, identifying millions of associations between targets, diseases, and drugs.

The paper introduces 'Associations on the Fly' (AOTF), a new feature in the Open Targets Platform that allows users to dynamically adjust evidence weights for target-disease associations.

The paper constructs a tissue-specific protein interaction atlas using proteomic data from 11 human tissues, identifying key drivers of inter-tissue variation and prioritizing disease genes in brain disorders.

This paper standardizes terminology for describing disease-gene relationships to improve variant classification and genetic test interpretation.

The paper describes the formation of the Gene Curation Coalition to standardize gene-disease validity terminology and create a unified database for curated gene-disease assertions.

This study uses whole-genome sequencing to identify regulatory and novel genic variants contributing to childhood cardiomyopathy.

The paper identifies gene-specific mutational signatures caused by replication errors and endogenous DNA damage using CRISPR-Cas9 knockouts and whole-genome sequencing.

This study demonstrates that whole genome sequencing can effectively detect repeat expansions in neurological disorders with high sensitivity and specificity, enabling diagnosis in previously undiagnosed patients.

The paper integrates gene essentiality data from human and mouse screens to classify genes into FUSIL categories, identifying developmental lethal genes associated with rare diseases.

The paper develops a machine learning model to predict genes causing rare neurological disorders and shows it accurately identifies disease-associated genes.