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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Feature graphs for interpretable unsupervised tree ensembles: centrality, interaction, and application in disease

Christel Sirocchi^1,2, Martin Urschler³, Bastian Pfeifer⁴

¹Department of Pure and Applied Sciences, University of Urbino, Urbino, 61029, Italy.

|February 15, 2025

Summary

We developed a new method to understand which features are most important in unsupervised machine learning models, improving interpretability for applications like disease subtyping.

Keywords:

Disease subtyping Feature graphs Feature selection Interpretable machine learning Unsupervised random forest

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Area of Science:

Artificial Intelligence
Bioinformatics
Computational Biology

Background:

Explainable AI (XAI) is crucial for trustworthy AI in healthcare.
Feature importance is key for model interpretability, especially in disease subtyping.
Evaluating feature contributions in unsupervised learning, like random forests, is challenging.

Purpose of the Study:

To introduce a novel methodology for enhancing the interpretability of unsupervised random forests.
To elucidate feature contributions using feature graphs derived from tree structures.
To present and evaluate feature selection strategies for effective feature combinations.

Main Methods:

Constructed feature graphs leveraging parent-child node splits in unsupervised random forest trees.
Developed feature selection strategies based on these graphs.
Evaluated the methodology on synthetic and benchmark datasets against state-of-the-art methods.
Applied the approach to kidney cancer gene expression data for patient subtyping.

Main Results:

The proposed methodology demonstrated superior performance, efficiency, reliability, and versatility.
Feature graphs provided valuable cluster-specific insights into feature contributions and interactions.
Clustering kidney cancer data using selected features identified three patient groups with distinct survival outcomes.

Conclusions:

The novel approach significantly enhances the interpretability of unsupervised random forests.
It enables effective feature selection for disease subtyping and patient stratification.
Identified patient subgroups and their associated molecular features facilitate targeted interventions and personalized medicine.