Enhancing transparency of omics data analysis with the Evolutionary Multi-Test Tree and Relative Expression

This paper presents a novel classification algorithm for multi-omics data, called Evolutionary Multi-Test Tree with Relative Expression (EMTTree+RX). The innovation lies in the model’s design, which integrates multi-test decision nodes with Relative Expression Analysis (RXA). Each decision node combines traditional univariate tests and top-scoring pair (TSP) comparisons, allowing the algorithm to capture complex relationships between features without relying solely on absolute values. This approach enables the proposed method to detect subtle patterns across various omics layers while maintaining a high level of interpretability, a feature crucial for clinical and bioinformatics applications. The tree structure is induced through Evolutionary Algorithms (EA), optimizing both the global architecture and local multi-test nodes to balance classification accuracy, test diversity, and feature cost. Applied to large-scale multi-omics datasets, where conventional decision tree methods often struggle with underfitting or overfitting, the proposed method consistently outperforms traditional models in terms of accuracy and transparency. This makes it a valuable tool for precision medicine and multi-modal data integration.