Lis Arend

POME provides graph-based embeddings for partially observed mixed-type biomedical data, achieving state-of-the-art imputation and enabling downstream analyses like subgroup discovery and predictive modeling.

This study expands the human plasma proteome analysis using an enhanced SomaScan platform, identifying new genetic associations and disease mechanisms beyond the classical secretome.

GNExT is a web-based platform that integrates GWAS data with systems medicine approaches to identify potential drug repurposing candidates and enable large-scale network medicine investigations.

The paper evaluates normalization methods in proteomics data, finding that some methods perform better than others and develops a tool to assess their impact.

The paper compares differential expression analysis methods for CyTOF data, finding that median-based methods are reliable for non-zero-inflated data while methods using full distributions handle zero-inflation better but may overpredict.