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

## TL;DR

This study uses deep learning to predict schizophrenia traits from genetic data, achieving 80% accuracy and showing promise for precision medicine.

## Contribution

The novel use of CNNs with advanced optimization techniques to predict schizophrenia phenotypes from exome sequencing data.

## Key findings

- A CNN model achieved 80% accuracy in predicting schizophrenia traits from genetic data.
- Advanced optimization techniques improved model performance and reduced overfitting.
- The study highlights the potential of deep learning in uncovering genotype-phenotype relationships in psychiatric disorders.

## 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.