Belén Otero-Carrasco

This study evaluates four large language models for validating drug repurposing hypotheses using structured prompts and benchmark cases, finding GPT-4o and DeepSeek most reliable with improved accuracy and precision.

This study identifies conserved sequence patterns in cancer-related proteins and their structural implications, providing insights into potential biomarkers and therapeutic targets.

This paper prioritizes potential drugs for repurposing by analyzing pathway-disease-drug relationships and network proximity using transcriptomic and PPI data.

The paper identifies drug repurposing cases based on biological pathways (DREBIOP) and finds they involve more pathways and lower gene-disease association values compared to target-based repurposing.

This paper improves drug repurposing by integrating 2D drug molecular structures into graph neural networks, achieving higher AUROC and AUPRC scores than previous methods.

This paper examines the evolution of orphan drug development and identifies biological patterns in rare diseases treated with repurposed drugs.

This study investigates gender differences in drug repositioning by analyzing adverse drug reactions, with a focus on rare diseases.

REDIRECTION predicts disease-drug links for drug repurposing using graph-based deep learning on biomedical data, achieving high AUROC and AUPRC scores.