Karl Kristian Krull

Deep learning-based proteomics enables accurate classification of bulk and single-cell samples

This paper introduces a deep learning model called Proformer for classifying bulk and single-cell proteomic samples, showing improved performance over existing methods.

Enhanced feature matching in single-cell proteomics characterizes response to IFN-gamma and reveals co-existence of different cell states

The paper introduces DIA-ME, a method for single-cell proteomics that improves sensitivity and data completeness, revealing co-existing cell states in response to IFN-gamma.