ArticlePLoS computational biology2022
Multi-omics subtyping of hepatocellular carcinoma patients using a Bayesian network mixture model.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Lung cancer multimodal auxiliary diagnosis based on entropy weight decision fusion.Biomedical engineering online · 2026Article
- Decoding disease and therapy through multiomics integration and systems analysis.Briefings in bioinformatics · 2026Review
- Beyond silencing: integrative multi-omics and spatial profiling unravel the systems-level role of piRNAs in HBV-driven Hepatocarcinogenesis.Molecular biology reports · 2026Review
- Explainable artificial intelligence and ensemble learning for hepatocellular carcinoma classification: State of the art, performance, and clinical implications.World journal of hepatology · 2025Review
- Comprehensive review of Bayesian network applications in gastrointestinal cancers.World journal of clinical oncology · 2025Review
- Network-based clustering unveils interconnected landscapes of genomic and clinical features across myeloid malignancies.Nature communications · 2025Article
- An Overview of Advances in Rare Cancer Diagnosis and Treatment.International journal of molecular sciences · 2024Review
- Article
- Discovering gene regulatory networks of multiple phenotypic groups using dynamic Bayesian networks.Briefings in bioinformatics · 2022Article
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Authors and funding
8 authors.
Funding
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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 hepatocellular carcinoma (HCC) dataset comprising genome (mutation and copy number), transcriptome, proteome, and phosphoproteome data. We identified three main HCC subtypes together with molecular characteristics, some of which are associated with survival even when adjusting for the clinical stage. Cluster-specific networks shed light on the links between genotypes and molecular phenotypes of samples within their respective clusters and suggest targets for personalized treatments.
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