Tom Röschinger

The paper introduces an algorithm to identify condition-dependent transcription factor binding sites by compressing promoter sequences into hyperletters, validated on E. coli data.

This paper introduces DARSI, a deep learning model that predicts gene expression and transcription factor binding sites from DNA sequences using MPRA data.

The paper identifies regulatory elements and transcription factors in E. coli genes across diverse environments using experimental and computational methods.

The paper creates synthetic single-cell gene expression data using thermodynamic models to better understand and optimize MPRA experiments for regulatory architecture inference.