Biologically Interpretable Deep Learning for Metabolomics

Abstract

Depression, a leading cause of disability worldwide, is challenging to diagnose due to its reliance on subjective clinical evaluations. Metabolomics, which analyzes small molecules to reflect physiological and pathological states, holds promise for enhancing the diagnosis and identifying biomarkers for depression, potentially leading to better understanding and treatment options. Despite the complexity of metabolomics data, deep learning methods have not been extensively explored due to issues with interpretability, which are crucial for gaining insights into biological mechanisms. This study evaluates a biologically interpretable deep neural network, MetaboNet, trained on metabolomics data, for predicting depression and identifying key metabolites and biochemical pathways relevant to the condition. Our results demonstrate that MetaboNet outperforms logistic regression, though the overall classification performance remains modest. Notably, the classification results revealed sex-related differences, with better performance observed in females. Our findings do not support the capability of MetaboNet to identify biologically relevant individual metabolites. However, MetaboNet shows promise in identifying biochemical sub-pathways and super-pathways relevant to depression, which are validated by existing literature.