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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.
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.
