Florian Buettner

Deep proteomics of 997 treatment-naïve colorectal cancers defines six subgroups, splitting chromosomally unstable tumors into three and revealing a rare adverse subgroup, with H&E slide benchmarks.

PACMON integrates multi-omics data from perturbation screens to infer pathway-level responses to experimental perturbations, achieving high accuracy and scalability.

This paper develops a continual learning framework to map colorectal cancer cell states to causal mechanisms through integration of single-cell data and perturbation experiments.

MOFA-FLEX is a flexible factor analysis framework that integrates multi-omics data with prior knowledge, improving robustness and disentangling variation in scRNA-seq and CITE-seq data.

This study identifies differentially explained genes in heart failure subtypes using neural networks and XAI tools to improve understanding of molecular mechanisms.

This paper introduces a computer vision platform that detects live mutant blood cells with specific mutations using morphology recognition, without requiring nucleic acid sequencing.

This study uses barcoding and multiome single-cell data to show a unidirectional differentiation hierarchy in B-ALL with an immature population having high leukemogenic potential.

This paper introduces a model that combines expressive representation learning with interpretable Gaussian processes to analyze single-cell multi-omics data, revealing relationships between cell clusters and marker genes.

The paper introduces MUSIC, a Bayesian tensor decomposition framework for integrating multi-modal molecular data, showing it reveals interpretable patterns across patients, cell types, and modalities.

The paper uses single-cell RNA-seq of differentiating iPS cells from 125 donors to identify expression quantitative trait loci influencing gene expression during endoderm differentiation.

The paper uses single cell triple-omics analysis to reveal how epigenetic changes drive germ layer specification in mouse embryos, showing distinct epigenetic priming and remodeling in different lineages.

MOFA, a sparse Bayesian factor model with mixed likelihoods, separates shared from modality-specific multi-omic variation, recovering IGHV, trisomy 12 and oxidative-stress axes in 200 leukaemia samples.

This paper introduces a scalable latent-factor model for scRNA-seq data that separates biological drivers from confounding effects using pathway annotations and improves gene set annotations.