Integrative analyses of multi-omic patient datasets are crucial to uncover disease subtypes, yet challenges arise from modality‑specific variability and missing data. We propose MLModNet, a multilayer network framework for robust patient stratification. MLModNet employs an extended resampling‑based method (Pareto‑COGENT) to build stable, informative, and modality‑specific patient similarity networks, integrates them into a multiplex network including patients missing individual assays, and detects patient stratification via multiplex‑adapted Leiden clustering.
We applied MLModNet to the COVID‑19 Multi‑omic Blood Atlas (COMBAT) dataset, integrating proteomics, transcriptomics, and cytometry. MLModNet discovered five patient endotypes that refine clinical WHO severity categories, each exhibiting distinct immune‑metabolic signatures involving IL‑33, TREM1, interferon response pathways, and shifts in cell proportions. Clinically, MLModNet clusters significantly stratified ICU‑free survival, and early cluster assignment probabilities predicted subsequent clinical markers (CRP, D‑dimer, Acuity scores). External validation on an independent Olink dataset confirmed the reproducibility of these endotypes and their prognostic relevance. Extensive ablation analyses further supported the robustness of the identified clusters.
MLModNet thus provides a scalable strategy to translate heterogeneous, incomplete multi‑modal data into biologically meaningful, clinically actionable patient stratifications.