Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
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SHR Neuro Krebs Kardio Lipid Stoffw Microb
Pfeifer, B; Schimek, MG.
A hierarchical clustering and data fusion approach for disease subtype discovery.
J Biomed Inform. 2021; 113:103636
Doi: 10.1016/j.jbi.2020.103636
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- Führende Autor*innen der Med Uni Graz
- Pfeifer Bastian
- Co-Autor*innen der Med Uni Graz
- Schimek Michael
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- Abstract:
- Recent advances in multi-omics clustering methods enable a more fine-tuned separation of cancer patients into clinical relevant clusters. These advancements have the potential to provide a deeper understanding of cancer progression and may facilitate the treatment of cancer patients. Here, we present a simple hierarchical clustering and data fusion approach, named HC-fused, for the detection of disease subtypes. Unlike other methods, the proposed approach naturally reports on the individual contribution of each single-omic to the data fusion process. We perform multi-view simulations with disjoint and disjunct cluster elements across the views to highlight fundamentally different data integration behavior of various state-of-the-art methods. HC-fused combines the strengths of some recently published methods and shows superior performance on real world cancer data from the TCGA (The Cancer Genome Atlas) database. An R implementation of our method is available on GitHub (pievos101/HC-fused).
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- Find related publications in this database (Keywords)
- Multi-omics
- Integrative clustering
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