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Related Experiment Video

Updated: May 16, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Machine learning-based integration identifies a 10-gene predictive signature and its classification patterns in

Yan Li1, Qing Sun2, Ye Shen1

  • 1Department of Epidemiology, School of Public Health, Beihua University, Jilin, 132013, Jilin , China.

European Archives of Psychiatry and Clinical Neuroscience
|May 14, 2026
PubMed
Summary

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The genetic basis of schizophrenia is strongly supported by family and twin studies.

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Researchers identified key genes and developed a machine learning model to create a diagnostic signature for schizophrenia (SCZ). This signature, along with a nomogram, shows promise for accurate SCZ prediction and personalized medicine approaches.

Area of Science:

  • Genetics and Genomics
  • Computational Biology
  • Psychiatric Disorders

Background:

  • Schizophrenia (SCZ) is a complex psychiatric disorder with high heritability but unclear etiology.
  • Current diagnostic and treatment biomarkers for SCZ are limited, hindering effective predictive, preventive, and personalized medicine (PPPM/3PM).

Purpose of the Study:

  • To identify novel biomarkers for schizophrenia (SCZ) diagnosis and treatment.
  • To develop a robust diagnostic signature and predictive model for SCZ using machine learning.
  • To investigate SCZ subtypes for tailored 3PM strategies.

Main Methods:

  • Differential gene expression (DEGs) and weighted gene co-expression network (WGCNA) analyses were performed on brain datasets.
  • A machine learning (ML) framework with 12 MLs and 84 combinations was used to construct a consensus diagnostic signature.
Keywords:
Immune infiltrationMachine learningNonnegative matrix factorization (NMF)Predictive preventive personalized medicine (PPPM/3PM)SchizophreniaUnsupervised clusteringWeighted gene co-expression network (WGCNA)

Related Experiment Videos

Last Updated: May 16, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

  • Consensus clustering and non-negative matrix factorization (NMF) were applied to identify SCZ subtypes.

    • Main Results:

    • 53 SCZ-key genes were identified, leading to a consensus diagnostic signature with high discriminative performance.
    • A nomogram model was established for quantitative SCZ prediction in clinical practice.
    • SCZ patients were classified into two distinct subtypes with unique immune and metabolic profiles.

    Conclusions:

    • A novel diagnostic signature and nomogram were developed, offering high accuracy for SCZ diagnosis.
    • Identified SCZ subtypes exhibit distinct inflammatory, immune, and metabolic patterns.
    • Integrating SCZ subtypes into the 3PM framework presents opportunities for enhanced clinical intelligence and management.