Alejandro Rodríguez‐González

The paper identifies shared amino acid patterns in cancer-related proteins and explores their potential for drug interaction analysis, focusing on ALDH2 and disulfiram.

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 examines how disease module significance depends on module size and Gene-Disease Association scores, suggesting cutoffs for robust module construction.

This study benchmarks docking tools on experimental and AI-predicted protein structures to assess their accuracy in simulating protein-ligand interactions.

This study uses scRNA-seq and network analysis to identify cell-type-specific transcriptional changes in Alzheimer's disease, revealing dysregulation in neurons and key roles of vascular cells.

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 work evaluates the confidence of deep learning models in predicting protein folding using two proteins with four domains of unknown function.

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.

The paper uses clustering to analyze disease similarities based on biological and phenotypical features, finding that weighted biological similarities can improve disease classification.

DISNET extracts phenotypic disease information from public sources to create customizable disease networks for drug repositioning.

This paper addresses gaps in UMLS disease mappings by using alternative expert-curated sources, resolving over 50% of missing mappings for some vocabularies.

The paper reviews the evolution of disease networks and evaluates their functional units to inform drug repurposing and prediction model development.