Daniel P. Lewinsohn

The paper introduces MeRN, a graph-guided variational autoencoder for learning single-cell metabolic states from transcriptomes, and identifies metabolic drivers in various biological contexts.

This study uses a deep-learning model to analyze scRNA-seq data and identifies how folate deficiency disrupts metabolic transitions in developing mouse neural ectoderm, leading to morphological defects.

PSAP-genomic-regions improves variant prioritization in whole genome sequencing by incorporating functional genomic regions, enhancing detection of both coding and non-coding pathogenic variants in rare disease diagnosis.

This paper introduces a GCN-based method for automating and interpreting scRNA-seq cell type annotation, showing improved accuracy over consensus and neighbor majority approaches.