Nils Körber

This paper presents a framework for peptide identification using commercial nanopore sequencing platforms, achieving single-amino-acid resolution and reliable performance across sequencing runs.

This paper introduces a deep learning model to identify virus-infected cells from light microscopy images without using labels or stains, achieving up to 96% classification accuracy.

The paper introduces seq2squiggle, a transformer-based model for simulating nanopore sequencing signals, which outperforms existing tools on real data.