NetMUG: a novel network-guided multi-view clustering workflow for dissecting genetic and facial heterogeneity.

ABSTRACT: Introduction: Multi-view data offer advantages over single-view data for characterizing individuals, which is crucial in precision medicine toward personalized prevention, diagnosis, or treatment follow-up. Methods: Here, we develop a network-guided multi-view clustering framework named netMUG to identify actionable subgroups of individuals. This pipeline first adopts sparse multiple canonical correlation analysis to select multi-view features possibly informed by extraneous data, which are then used to construct individual-specific networks (ISNs). Finally, the individual subtypes are automatically derived by hierarchical clustering on these network representations. Results: We applied netMUG to a dataset containing genomic data and facial images to obtain BMI-informe

SUBMITTER: Li Z 

PROVIDER: S-EPMC10731261 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature