Lorin M. Towle-Miller

MOSCATO selects clinically relevant features from multi-omic single-cell data using regularized tensor regression and is validated on leukemia data.

The paper introduces bFMD, a module detection algorithm that identifies balanced functional modules in genomic data, showing favorable performance in simulations and interpretable results in RNA-seq data.

The paper introduces a gene filtering method to improve supervised network analysis by removing irrelevant features, enhancing scalability and accuracy of downstream analyses like sparse canonical correlation analysis.