Antonio Berenguer

The paper identifies molecular signatures of Alzheimer's disease in AD mouse models and finds drugs that reverse these signatures and cognitive impairment.

The paper compares rotation-based scores for gene set analysis and finds the average of absolute values is the most powerful score.

The paper introduces deep neural networks to infer bioactivity signatures for uncharacterized compounds, improving activity prediction across various biological endpoints.

The paper introduces Chemical Checker, a resource that integrates bioactivity data on 800,000 small molecules and demonstrates its use in drug discovery and disease modeling.

The paper addresses technical biases in gene signature risk assessment in cancer cohorts by proposing a method to correct for systematic variability, ensuring accurate survival associations.