Computational Intelligence for Predicting Genetic Psychiatric Disorder

The psychological health of a person is as important as his physical health. To have a sound physical body, doctors and scientists have developed many drugs and treatments that can identify diseases before they occur in the body by identifying a person's genetic code. But the same advancements in psychological health have not been achieved till now. The mental and psychological health of many people has never been given the attention it deserves, but scientists have now recognized that many psychiatric disorders run in families, suggesting that genetic factors may be involved. Such disorders include autism, attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depression, and schizophrenia. Psychiatric syndromes can overlap, making it difficult to distinguish between them. The shared symptoms suggest they may also share similarities at the biological level. Studies have reported limited evidence of shared genetic risk factors, such as those between schizophrenia and bipolar disorder, autism and schizophrenia, and depression and bipolar disorder. Genome sequencing can lead to the early detection of these disorders, which can lead to the provision of appropriate care at the right time, thereby saving many lives. Scientists can also develop medicines to cure these psychiatric disorders based on the identification of biological markers in genome sequencing.

In this Research Topic, we call the researchers to submit their original research in identifying psychiatric disorders from biological markers using computational intelligence models. Topics of interest include, but are not limited to:

• Machine learning and deep learning algorithms for predicting genetic psychiatric disorders

• Computational models of gene expression and regulatory networks in psychiatric disorders

• Use of genetic and epigenetic data for predicting psychiatric disorders

• Integration of multimodal data sources (e.g., neuroimaging, behavioral, and clinical data) for prediction

• Development and validation of predictive models for specific psychiatric disorders (e.g., depression, schizophrenia, bipolar disorder, anxiety disorders)

• Application of computational intelligence to personalized medicine for psychiatric disorders.

• Computational models for identifying genetic risk factors for psychiatric disorders

• Artificial intelligence applications in psychiatric genomics

• Clinical decision support systems for personalized psychiatric treatment based on genetic risk factors

• Predictive modeling of psychiatric disorders using biological and environmental factors

• Data mining techniques for the identification of genetic variants associated with psychiatric disorders

• Genomic biomarkers for the detection of psychiatric disorders

Submissions will be subject to a rigorous peer-review process to ensure the highest scientific quality of the papers selected for publication. All papers must be original and not under review elsewhere.