Caleb M. Radens

The paper introduces PanExonNet, a deep learning framework that predicts splicing patterns by integrating sequence and splicing factor expression, showing improved context specificity and generalization to new cell types.

This paper introduces LSV-seq, a targeted RNA-seq method optimized with machine learning to detect alternative splicing with high sensitivity and low sequencing depth.

The paper introduces MAJIQ v2, a tool for analyzing RNA splicing in large and heterogeneous RNA-seq datasets, showing improved performance over existing methods and applying it to brain subregion-specific splicing analysis.

This study shows that CD28 costimulation enhances alternative splicing of apoptosis-related genes, promoting T cell survival through combinatorial effects on caspase-9, Bax, and Bim.

MOCCASIN corrects known and unknown confounders in RNA splicing analysis using synthetic and real data from TARGET and ENCODE.