Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
Gewählte Publikation:
SHR Neuro Krebs Kardio Lipid Stoffw Microb
Pfeifer, B; Bloice, MD; Schimek, MG.
Parea: Multi-view ensemble clustering for cancer subtype discovery.
J Biomed Inform. 2023; 143:104406
Doi: 10.1016/j.jbi.2023.104406
Web of Science
PubMed
FullText
FullText_MUG
- Führende Autor*innen der Med Uni Graz
- Pfeifer Bastian
- Co-Autor*innen der Med Uni Graz
- Bloice Marcus
- Schimek Michael
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- Abstract:
- Multi-view clustering methods are essential for the stratification of patients into sub-groups of similar molecular characteristics. In recent years, a wide range of methods have been developed for this purpose. However, due to the high diversity of cancer-related data, a single method may not perform sufficiently well in all cases. We present Parea, a multi-view hierarchical ensemble clustering approach for disease subtype discovery. We demonstrate its performance on several machine learning benchmark datasets. We apply and validate our methodology on real-world multi-view patient data, comprising seven types of cancer. Parea outperforms the current state-of-the-art on six out of seven analysed cancer types. We have integrated the Parea method into our Python package Pyrea (https://github.com/mdbloice/Pyrea), which enables the effortless and flexible design of ensemble workflows while incorporating a wide range of fusion and clustering algorithms.
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- Find related publications in this database (Keywords)
- Multi-view clustering ensemble clustering
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