Integrating AI and genomics: predictive CNN models for schizophrenia phenotypes.

Journal: Journal of integrative bioinformatics
Published Date: Jun 18, 2025

Abstract

This study explores the use of deep learning to analyze genetic data and predict phenotypic traits associated with schizophrenia, a complex psychiatric disorder with a strong hereditary component yet incomplete genetic characterization. We applied Convolutional Neural Networks models to a large-scale case-control exome sequencing dataset from the Swedish population to identify genetic patterns linked to schizophrenia. To enhance model performance and reduce overfitting, we employed advanced optimization techniques, including dropout layers, learning rate scheduling, batch normalization, and early stopping. Following systematic refinements in data preprocessing, model architecture, and hyperparameter tuning, the final model achieved an accuracy of 80 %. These results demonstrate the potential of deep learning approaches to uncover intricate genotype-phenotype relationships and support their future integration into precision medicine and genetic diagnostics for psychiatric disorders such as schizophrenia.

Authors

  • Guilherme Henriques
    Department of Informatics Engineering, University of Coimbra, CISUC/AC - Centre for Informatics and Systems of the University of Coimbra, Coimbra, Portugal.
  • Maryam Abbasi
    Griffith School of Engineering, Griffith University, Nathan, QLD, Australia. Electronic address: m.abbasi@griffith.edu.au.
  • Daniel Martins
    Centre for Informatics and Systems, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal.
  • Joel P Arrais

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