Multi-omics subtyping of hepatocellular carcinoma patients using a Bayesian network mixture model.

ABSTRACT: Comprehensive molecular characterization of cancer subtypes is essential for predicting clinical outcomes and searching for personalized treatments. We present bnClustOmics, a statistical model and computational tool for multi-omics unsupervised clustering, which serves a dual purpose: Clustering patient samples based on a Bayesian network mixture model and learning the networks of omics variables representing these clusters. The discovered networks encode interactions among all omics variables and provide a molecular characterization of each patient subgroup. We conducted simulation studies that demonstrated the advantages of our approach compared to other clustering methods in the case where the generative model is a mixture of Bayesian networks. We applied bnClustOmics to a hepatocellul

SUBMITTER: Suter P 

PROVIDER: S-EPMC9481159 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature