Samuel Müller

eNODAL is a clustering method for nutriomics data that integrates experimental design with unsupervised learning to identify interpretable clusters of omics features affected by drug-diet interactions.

The paper introduces a method to handle omics data variation across platforms and times, showing its use in three disease risk models for clinical application.

SurvBenchmark evaluates survival analysis methods on clinical and omics data, showing model performance varies with metrics and datasets.