Enhancing multi-omics data classification with relative expression analysis and decision trees

Abstract

This study introduces the Relative Multi-test Classification Tree (RMCT), a novel classification method tailored for multi-omics data analysis. The RMCT method combines the interpretative power of decision trees with the analytical precision of Relative eXpression Analysis (RXA) to address the complex task of examining biomedical data derived from diverse high-throughput technologies. The proposed RMCT approach discerns patterns within and across omics layers, yielding an accurate and interpretable classifier. In each internal node of RMCT, we create a multitest - group of Top-Scoring-Pair tests, that capture the ordering relationships among features from various omics. Multi-tests are optimized for maximal reduction of Gini impurity, and ensuring consistency in decision-making. We address computational challenges by advanced GPU parallelization, remarkably improving RMCT’s time performance. Through experimental validation on diverse multi-omics datasets, RMCT has demonstrated superior performance compared to traditional tree-based solutions, particularly in terms of accuracy and clarity of predictions. This method effectively reveals intricate interactions and relationships within multi-omics data, marking it as a useful addition to bioinformatics and biomedicine. This work represents a thorough extension of our preliminary research, which was initially presented at the twenty-third edition of the International Conference on Computational Science (ICCS). It expands the initial concept of integrating decision trees with RXA for multi-omics data classification, deepening the analytical methodologies, further optimizing the GPU computing, and broadening the experimental validation.