Gaining Biological Insights through Supervised Data Visualization

Abstract excerpt

Dimensionality reduction-based data visualization is pivotal in comprehending complex biological data. The most common methods, such as PHATE, t-SNE, and UMAP, are unsupervised and therefore reflect the dominant structure in the data, which may be independent of expert-provided labels. Here we introduce a supervised data visualization method called RF-PHATE, which integrates expert knowledge for further exploratio...

Topics

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• Algorithms
• Asthma
• Computer Graphics
• Cytokines
• Data Mining
• Neural Networks, Computer
• Severity of Illness Index
• User-Computer Interface

Identifiers and source

Literature Corpus work

30ae68c1-61fd-5352-976e-6a038ba41e87

DOI

10.1101/2023.11.22.568384