Jan Krumsiek

SAHA creates a multimodal, subcellular-resolution reference atlas of healthy human tissues, revealing spatial organization and disease-associated disruptions across multiple organ systems.

AutoFocus identifies multi-omic disease-associated modules across biological scales using a hierarchical framework, applied to type 2 diabetes and Alzheimer's datasets.

The paper identifies sex-specific metabolomic differences in serum and brain of Alzheimer's disease mouse models, showing model-specific metabolic signatures that align with human APOE4 carriers.

piTracer identifies synergistic drug targets by analyzing metabolomics data to reconstruct molecular cascades and predict combination therapies.

rox is a statistical model that handles missing values in omics data by treating them as low concentrations, outperforming existing methods on simulated and metabolomics datasets.

This study identifies multi-omics prognostic signatures for prostate cancer using transcriptomics and metabolomics data, showing their ability to stratify patients into distinct risk groups.

This study identifies widespread metabolic dysregulation in Alzheimer's disease brain tissue, linking it to tau pathology and highlighting potential pathomechanisms.

SGI identifies clinical subgroups in omics datasets using hierarchical clustering and association testing, demonstrated on a metabolomics dataset from a type 2 diabetes study.

This study trains a variational autoencoder on metabolomics data to learn nonlinear latent representations and validates its generalizability across different disease cohorts.

This paper introduces a cutoff selection algorithm for correlation networks that uses prior knowledge to improve network reconstruction, showing better performance than statistical cutoffs across multiple datasets.

The paper evaluates normalization methods for glycomics data by assessing how well they enable accurate reconstruction of known glycosylation pathway reactions using Gaussian Graphical Models.

The paper investigates sex and APOE ε4 genotype effects on metabolic alterations in Alzheimer’s disease using stratified analysis of serum metabolites.

The paper identifies gene expression patterns linked to Cesarean section and islet autoimmunity, showing shared transcriptional changes associated with immune cell activation.

The paper evaluates missing data handling strategies in untargeted metabolomics data, finding that KNN imputation with variable pre-selection is robust across multiple evaluation schemes.

MoDentify identifies regulated metabolomics modules driven by phenotypes across different resolution levels, offering higher statistical power than classical methods.