Work

Beyond Feature Selection: Interpretable Machine Learning for Mechanistic Insights in Metabolomics

Year: 2026

Type: article

Abstract: While metabolomics captures the dynamic chemical landscape of biological systems, its inherent high dimensionality and complexity pose significant analytical hurdles. Interpretable Machine Learning (IML) is revolutionizing the field by moving beyond traditional feature selection to extract biologically meaningful insights alongside robust predictions. This review systematically examines IML's application in metabolomic biomarker discovery. We highlight how interpretation frameworks decode the key metabolites driving model decisions, transforming opaque "black-box" algorithms into testable mechanistic hypotheses. By evaluating cutting-edge studies across various pathologies, we illustrate IML's pivotal role in disease subtyping, early diagnosis, treatment prediction, and mitigating demographic disparities. Although challenges in data generalizability persist, IML remains an indispensable bridge between computational prediction and biological understanding, ultimately advancing precision medicine.

Source: Biology

DOI: 10.3390/biology15070572

Authors: Haotian Bai, Yufei Ren, Jihan Wang, Yangyang Wang, Yanning Yang

Institution: Yan'an University

Language: English

Topic: Metabolomics and Mass Spectrometry Studies

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,258