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Parea: Multi-view ensemble clustering for cancer subtype discovery.

Bastian Pfeifer¹, Marcus D Bloice¹, Michael G Schimek¹

¹Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Austria.

Journal of Biomedical Informatics

|May 31, 2023

Summary

Parea, a novel multi-view hierarchical ensemble clustering method, effectively discovers disease subtypes by integrating diverse patient data. This approach outperforms existing methods in identifying cancer patient sub-groups with similar molecular characteristics.

Keywords:

Disease subtyping Hierarchical clustering Multi-omics Multi-view clustering ensemble clustering

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Area of Science:

Computational biology
Bioinformatics
Machine learning

Background:

Multi-view clustering is crucial for stratifying patients based on molecular characteristics.
Existing methods struggle with the high diversity of cancer data.
A robust approach is needed for accurate disease subtype discovery.

Purpose of the Study:

To introduce Parea, a multi-view hierarchical ensemble clustering approach.
To enable effective disease subtype discovery from complex patient data.
To improve upon current state-of-the-art clustering methods.

Main Methods:

Developed Parea, a multi-view hierarchical ensemble clustering algorithm.
Validated Parea on machine learning benchmark datasets.
Applied and tested Parea on real-world multi-view patient data from seven cancer types.

Main Results:

Parea demonstrated superior performance compared to state-of-the-art methods.
The method achieved better results on six out of seven analyzed cancer types.
Performance was validated on diverse real-world cancer datasets.

Conclusions:

Parea offers a powerful and flexible approach for multi-view patient data analysis.
The method enhances disease subtype discovery in oncology.
The Parea method is available in the open-source Python package Pyrea.