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Integrative Molecular Pattern Learning for Mental Disorders Via Dual-Effect Matrix-Enabled Multiomics Platform.

Wantong Zhang¹, Man Zhang¹, Yanchao Zhang¹

¹Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Department of Chemistry, Institutes of Biomedical Sciences, Fudan University, Shanghai, 201399, China.

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|February 22, 2026

Summary

This study introduces a novel multiomics platform for early mental disorder screening. It accurately identifies major depressive disorder, bipolar disorder, schizophrenia, and anxiety disorder using serum biomarkers and machine learning.

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Area of Science:

Biochemistry
Genomics
Psychiatry

Background:

Mental health disorders significantly impact well-being and social functioning, necessitating early and objective screening.
Current diagnostic methods often lack objectivity and timeliness, hindering effective intervention and leading to severe social consequences.

Purpose of the Study:

To develop a high-throughput multiomics platform for analyzing serum metabolomic and peptidomic profiles.
To utilize advanced machine learning algorithms for the objective diagnosis of four major mental disorders in adults and adolescents.
To identify shared and disorder-specific biomarkers and dysregulated biological pathways.

Main Methods:

Development of a high-throughput multiomics platform using a dual-effect matrix.
Acquisition and analysis of serum metabolomic and peptidomic data from patients with major depressive disorder, bipolar disorder, schizophrenia, anxiety disorder, and healthy controls.
Application of machine learning algorithms for diagnostic classification and identification of biomarkers.
Multiomics pathway analysis to reveal dysregulated biological pathways.

Main Results:

Achieved an overall diagnostic area under the curve (AUC) of 0.998 for distinguishing patients with the four mental disorders from healthy controls.
Attained an average AUC of 0.974 for the specific classification of each individual mental disorder.
Identified two dysregulated biological pathways through multiomics pathway analysis.
Discovered age-independent, disease-specific metabolic and peptide features.

Conclusions:

The developed multiomics platform and machine learning approach show high accuracy in diagnosing major mental disorders.
This technology represents a significant advancement in objective diagnostic tools for psychiatric disorders.
The findings contribute to a deeper mechanistic understanding of mental disorders and pave the way for next-generation diagnostic solutions.