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Biological Causes of Schizophrenia

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Schizophrenia, a severe psychiatric disorder, arises from a complex interplay of biological factors, including genetic predisposition, structural brain abnormalities, neurotransmitter dysregulation, and developmental irregularities. These factors collectively contribute to the onset and progression of the disorder, which typically manifests in late adolescence or early adulthood.
Genetic Factors in Schizophrenia
The genetic basis of schizophrenia is strongly supported by family and twin studies.

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A Strategy to Identify de Novo Mutations in Common Disorders such as Autism and Schizophrenia

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Ensemble Learning for Higher Diagnostic Precision in Schizophrenia Using Peripheral Blood Gene Expression Profile.

Vipul Vilas Wagh¹, Tanvi Kottat¹, Suchita Agrawal²

¹Symbiosis School of Biological Sciences, Symbiosis International (Deemed University), Pune, MH, India.

Neuropsychiatric Disease and Treatment

|May 8, 2024

Summary

This study developed an ensemble learning approach for precise schizophrenia diagnosis using gene expression data. The models achieved 80.41% precision, offering a more accurate diagnostic tool for schizophrenia.

Keywords:

Schizophrenia ensemble learning gene expression machine learning peripheral blood

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Area of Science:

Genomics
Computational Biology
Psychiatric Diagnostics

Background:

Schizophrenia (SCZ) diagnosis is burdened by stigma, necessitating higher precision.
Reducing diagnostic false positives is crucial for patient well-being and clinical practice.

Purpose of the Study:

To develop an ensemble learning-based approach for high-precision diagnosis of SCZ.
To utilize peripheral blood gene expression profiles for diagnostic modeling.

Main Methods:

Machine learning models (SVM, PAM) were trained on differentially expressed genes (DEGs).
A voting ensemble classifier combined SVM and PAM for SCZ sample classification.
Cross-platform compatibility was assessed by classifying RNA sequencing (RNA-Seq) data.

Main Results:

Ensemble learning achieved 80.41% precision, outperforming individual SVM (71.69%) and PAM (77.20%) models.
RNA-Seq data classification yielded moderate precision (59.92%).
Key biological processes identified include response to stress and immune system regulation, with hub genes like RBX1 and CUL4B.

Conclusions:

Robust models for enhanced diagnostic precision in psychiatric disorders were developed.
Future work will focus on multi-omic integration and explainable AI for diagnostics.