Laura D. Martens

scooby models scRNA-seq and scATAC-seq data from DNA sequence to capture cell-specific gene regulation and variant effects.

The paper introduces muBind, a deep learning model that predicts genomic counts from DNA sequences and cell relationships, enhancing understanding of regulatory dynamics during cell transitions.

This study shows that using fragment counts instead of binarized data improves scATAC-seq analysis methods like clustering and batch integration.

The paper shows codon optimality affects mRNA stability across human tissues, with energy metabolism and age modulating this effect.

The paper develops machine and deep learning models to predict chromatin interactions and TAD boundaries using histone modification data, independent of Hi-C data.