Class Dominancy Profiles in Multi-class and Multi-cluster Similarity Networks

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00098892%3A_____%2F25%3A10159372" target="_blank" >RIV/00098892:_____/25:10159372 - isvavai.cz</a>
Alternative codes found
RIV/61989100:27240/25:10257850
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-031-82439-5_2" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-82439-5_2</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-82439-5_2" target="_blank" >10.1007/978-3-031-82439-5_2</a>

Result language
angličtina
Original language name
Class Dominancy Profiles in Multi-class and Multi-cluster Similarity Networks
Original language description
The visual representation of data analysis results, a current trend in data science, is essential for people without deep knowledge of statistics and machine learning. Patient Similarity Networks (PSNs), as an instance of similarity networks in general, are commonly used visualization tools in biomedical data analysis. PSNs are an understandable presentation of the complex relationships hidden in patient data, such as partitioning patients with similar characteristics into clusters, providing relatively easy interpretation to clinicians and clinical biologists. However, the interpretation may be inaccurate due to an incorrect intuition, especially in complex multi-class, multi-cluster situations. Our paper focuses on analyzing cluster-class relationships in PSNs, assuming different numbers of classes and clusters, both of different and potentially imbalanced sizes. We use several problematic situations to show how to support clinical intuition in interpreting these situations correctly. We use the Matthews correlation coefficient to analyze cluster-class relationships in a way that understandably complements the PSN visualization. Finally, we present a visualization of this approach on a real-world patient similarity network.
Czech name
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Czech description
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Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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