Meghamala Sinha

This paper introduces DBR-X, an explainable GNN framework for drug repositioning that outperforms existing models in predicting drug-disease associations and provides interpretable biological explanations.

This paper introduces a weighted ensemble method for knowledge graph completion that improves drug-disease candidate discovery by combining knowledge graph embeddings and path-based reasoning.

This paper evaluates seven link prediction methods on a biomedical knowledge graph to identify drug repurposing candidates by combining path-based reasoning with knowledge graph embedding approaches.

ARAX is a graph-based reasoning tool that integrates knowledge across sources to answer translational biomedical questions.

RTX-KG2 integrates 70 biomedical knowledge sources into a standards-compliant knowledge graph with a Biolink schema, providing downloadable and queryable versions via API.

The paper introduces a method called 'Learn and Vote' to improve causal network learning from mixed observational and interventional data, showing it is more accurate than existing methods on larger datasets.