2 papers
cs.LG2025
UnPaSt: unsupervised patient stratification by biclustering of omics data
Michael Hartung, Andreas Maier, Yuliya Burankova +25
Unsupervised patient stratification is essential for disease subtype discovery, yet, despite growing evidence of molecular heterogeneity of non-oncological diseases, popular method…
cs.MS2025
NApy: Efficient Statistics in Python for Large-Scale Heterogeneous Data with Enhanced Support for Missing Data
Fabian Woller, Lis Arend, Christian Fuchsberger +2
Existing Python libraries and tools lack the ability to efficiently compute statistical test results for large datasets in the presence of missing values. This presents an issue as…