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SHR Neuro Cancer Cardio Lipid Metab 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 [OPEN ACCESS]
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Leading authors Med Uni Graz: Pfeifer Bastian
Co-authors 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).
Find related publications in this database (using NLM MeSH Indexing): Algorithms - administration & dosage; Cluster Analysis - administration & dosage; Databases, Factual - administration & dosage; Humans - administration & dosage; Neoplasms - genetics
Find related publications in this database (Keywords): Multi-omics; Integrative clustering; Multi-view clustering; Disease subtyping