Jon Sánchez-Valle

This paper uses generative and symbolic AI to forecast kidney cancer progression from cross-sectional data, providing interpretable models and molecular insights.

The paper identifies genetic correlations between neuropsychiatric disorders and cancers, revealing shared and opposing biological mechanisms.

EBEx prioritizes disease-relevant genes from transcriptomic data in COPD by integrating data-driven signals with curated gene sets and network-based expansion, identifying biologically relevant candidates and molecular subgroups.

The paper identifies sex-specific transcriptomic similarities between comorbid diseases and finds that 13-16% of disease pairs show sex-specific transcriptome similarity, recapitulating 53-60% of known sex-specific comorbidities.

The paper develops a method to generate a disease network using RNA-seq data, revealing molecular mechanisms behind comorbidities and identifying new associations through patient stratification.

The paper uses matrix factorization to analyze molecular mechanisms behind the inverse comorbidity between Alzheimer’s disease and lung cancer, identifying new candidate factors and subtype-specific mechanisms.

The paper compares gene expression profiles of ASD and 22 cancer types, finding shared and opposing deregulations in specific cancers and common biological pathways.

The paper constructs a disease interaction network from patient molecular profiles to uncover molecular mechanisms of comorbidity and identify associated genes and drugs.