Lauren Sanders

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

This study applies deep learning tools to automate behavioral analysis of mice in spaceflight, revealing progressive behavioral adaptations to microgravity.

The paper presents a framework for imputing sparse, multimodal space biology datasets and shows that MICE-Elastic and a hybrid strategy preserve biological signal better than other methods.

This paper benchmarks cross-species adaptation of a human retinal model to rodent OCT for age estimation, finding that CNN baselines outperform transformer models in small-sample settings.

The paper evaluates the CARE pipeline for identifying druggable targets in pediatric cancers using RNA-Seq data from 33 patients, showing 94% of cases had clinically significant findings.

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 identifies Systemic Genome Correlation Loss as a key biosignature of spaceflight by analyzing transcriptomic data from multiple datasets.

The paper evaluates the informativeness of reads in bulk RNA-Seq datasets and recommends using MEND reads for accurate gene expression quantification in pediatric cancer studies.