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miRNA-Based Diagnosis of Schizophrenia Using Machine Learning.

Vishrut Heda¹, Saanvi Dogra², Valentina L Kouznetsova^3,4

¹Scholars Program, CureScience Institute, San Diego, CA 92121, USA.

International Journal of Molecular Sciences

|March 13, 2025

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This summary is machine-generated.

Machine learning models using dysregulated microRNAs (miRNAs) show promise for diagnosing schizophrenia. A sequential neural network achieved 94.32% accuracy, offering a potential biomarker for psychiatric diagnosis.

Area of Science:

Neuroscience
Bioinformatics
Computational Biology

Background:

Current schizophrenia diagnostic methods lack reliability due to the absence of a stable biomarker.
Machine learning (ML) presents a promising avenue for improving the diagnosis of schizophrenia and other neurological disorders.

Purpose of the Study:

To investigate the utility of dysregulated microRNAs (miRNAs) as potential biomarkers for schizophrenia diagnosis using machine learning.
To develop and evaluate ML models for identifying schizophrenia based on miRNA expression profiles.

Main Methods:

Dysregulated miRNAs were extracted from public databases and curated from literature.
Datasets including selected miRNAs, gene targets, and pathways were used as features for ML models.
Models were trained and classified using WEKA and TensorFlow, with various classifiers evaluated.

Keywords:

diagnostics machine learning miRNA schizophrenia

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Main Results:

The developed Sequential Neural Network (SNN) model achieved the highest accuracy (94.32%) in classifying schizophrenia.
Naïve Bayes classifier showed the second-best performance with 72.23% accuracy.
Validation testing confirmed the SNN's robustness, achieving 88.88% accuracy, while Naïve Bayes achieved 72.22%.

Conclusions:

Dysregulated miRNAs, when analyzed with machine learning, demonstrate practical utility as a diagnostic aid for schizophrenia.
This approach holds potential for assisting physicians in diagnosing schizophrenia and possibly other neurological conditions.