Tudor Ionel Oprea

The paper describes the Common Fund Data Ecosystem (CFDE), which integrates NIH Common Fund data resources to enable discovery, analysis, and collaboration across heterogeneous datasets.

The paper prioritizes 3.7 million patents to identify those likely containing bioactivity data for understudied targets, aiming to curate them for the ChEMBL database.

The paper introduces a degree-aware edge sampling method to reduce performance inflation in biomedical graph representation learning caused by imbalanced edge degree distributions.

The paper constructs a reproductive toxicity knowledge graph (ReproTox-KG) to link birth defects, drugs, and genes, identifying potential molecular mechanisms for drug-induced birth defects.

The paper presents a workflow for integrating diverse datasets to build a network pharmacology resource for COVID-19 research, resulting in a Neo4j graph database named Neo4COVID19.

DISEASES 2.0 updates a disease-gene association database by integrating text mining, curated data, and GWAS, increasing text-mined associations by 9-fold.

The paper evaluates druggable genes in mice and finds that many understudied ones have significant phenotypes similar to human diseases, suggesting therapeutic potential.

The paper uses word embeddings from PubMed to predict which protein kinases are relevant for treating specific cancers using machine learning.

TIGA aggregates and ranks gene-trait associations from GWAS data to prioritize drug targets using statistical and bibliometric evidence.

The paper identifies sex-biased differential alternative splicing across multiple human tissues and links it to gene function and estrogen response.

SmartGraph is a network pharmacology platform that integrates drug-target interactions and pathway data to predict bioactivity and generate hypotheses for drug repurposing and mechanism-of-action.

The paper uses machine learning to identify potential autophagy-related genes, finding 23% of top predictions in existing databases and suggesting 193 genes as possible 'dark genes'.

The paper introduces the Drug Target Ontology (DTO) to classify and integrate drug discovery data by formalizing druggable protein targets and their characteristics.