Alexander Sasse

This paper introduces SAGE-net, a framework for training sequence-to-function models using personal genomes, showing improved gene expression prediction but limited generalization, while DNA methylation shows better generalization.

The paper improves sequence-to-activity models by incorporating ATAC-seq profiles, enhancing predictions of chromatin accessibility across immune cell types.

This paper introduces DeepAllele, a deep learning model for allele-specific gene regulation analysis, showing improved detection of cis-regulatory elements in F1 hybrid mice immune cells.

This study benchmarks deep neural networks for predicting gene expression from DNA sequence across individuals and identifies limitations in predicting variant effects.

The paper introduces a method to approximate in silico saturation mutagenesis using first-order Taylor expansion, reducing computational cost while maintaining robustness.

The paper inserts random 50-base sequences into yeast introns and finds that most reduce splicing efficiency, likely due to RNA secondary structures, but existing models poorly predict these effects.