Richard Röttger

This paper refines the Tangram spatial mapping tool to improve consistency and reliability in mapping single-cell RNA-seq data to spatial transcriptomics data.

The paper introduces nanoBERT, a transformer model for predicting amino acids in nanobody sequences, showing its advantage over general models and gene-specific statistics.

The paper analyzes variance sources in LC-MS data and evaluates transfer learning's impact on ML performance across public datasets.

The paper develops a deep learning method to quantify lipophagy in yeast using fluorescence and cryo-soft X-ray microscopy data.

MoSBi automates biclustering analysis across multi-omics data to identify robust molecular signatures for stratification and subtyping.

The paper introduces MS2AI, a pipeline that automates the collection and preparation of LC-MS data for machine learning, and presents a CNN for oxidized peptide identification.

The paper introduces Scellnetor, a tool to identify systems biology patterns in pseudo-temporal trajectories from scRNA-seq data, applied to hematopoiesis and CD8 T-cell development.