James A. Casaletto

The paper uses machine learning to identify molecular pathways associated with retinal damage in spaceflown mice, revealing distinct signatures for oxidative stress and apoptosis.

The paper uses machine learning to analyze spaceflight effects on mammary tissue, finding age-related differences in molecular pathways linked to breast cancer.

This paper presents a federated learning framework deployed on the International Space Station to train models using data from Earth and the ISS without transferring raw data.

This paper pre-trains a machine learning model on Perturb-Seq data to predict perturbation effects in new datasets, including spaceflight gene expression profiles.

The paper models how data sharing affects the classification of genetic variants, showing higher classification probabilities with shared data over time.

The paper demonstrates federated analysis of BRCA1 and BRCA2 variants in a Japanese cohort to interpret variants of uncertain significance without transferring sensitive data.