Núria Queralt-Rosiñach

This paper introduces rd-explainer, a graph-based explainable AI method for drug repurposing in rare diseases, which provides interpretable predictions and explanations through semantic graphs.

This study applies collaborative network analysis to Huntington's disease transcriptomics data to identify enriched pathways and generate hypotheses about disease mechanisms.

The paper integrates multi-omics data from CAKUT patients to identify collagen's role in disease development and provides containerized workflows for analysis.

The paper introduces the GA4GH Phenopacket schema to standardize the exchange of phenotypic data for precision medicine applications.

The paper develops a knowledge network to prioritize drug mechanisms for repositioning, finding it can predict mechanisms with high accuracy.

The paper describes Wikidata as a FAIR knowledge graph for life sciences, showcasing its breadth, tools for integration, and use cases in biomedical analysis.

The paper presents a structured review of NGLY1 Deficiency as a knowledge graph, improving hypothesis generation through FAIR principles and enabling collaborative curation.