AI and Neurogenomics: Innovations in Precision Medicine for Brain Disorders

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Human Genomics and Genetic Diseases".

Deadline for manuscript submissions: 20 September 2025 | Viewed by 698

Special Issue Editors


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Guest Editor
Genomic Medicine Laboratory UILDM, IRCCS Fondazione Santa Lucia, 00142 Rome, Italy
Interests: genetic counseling; neurogenetics; pharmacogenetics; rare disorders; genetic diagnosis
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
1. Genomic Medicine Laboratory UILDM, Santa Lucia Foundation, 00142 Rome, Italy
2. Forensic Genetics Laboratory, Department of Biomedicine and Prevention, Tor Vergata University, 00133 Rome, Italy
Interests: forensic genetics; genetic counselling; human identification; neurogenetics; prenatal and postnatal genetic diagnosis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The advent of artificial intelligence (AI) in genomic medicine has allowed the development of innovative protocols for the evaluation of patients, diagnosis, and management. In genomic medicine, the integration of these new technologies lays the basis for the automated evaluation of big genomic data. In fact, machine learning algorithms can help in decoding genomic variants associated with several brain diseases. Neurogenomics has demonstrated that the integration of genomic evaluation supports the application of precision medicine to patients affected by both multifactorial and mendelian neurological disorders, such as Alzheimer's, Parkinson's, schizophrenia, and neuromuscular disorders. By integrating multi-omics data—genomics, transcriptomics, and epigenomics—AI models have demonstrated interesting abilities. The application of AI models, under strict human control, supports the identification of disease-associated biomarkers, helps in the prediction of patient-specific responses to treatments, and accelerates drug discovery.

In this Special Issue, we welcome reviews and original articles covering many aspects of artificial intelligence applications in neurogenomics. These include, but are not limited to, new diagnostic, therapeutic, and neuroimaging protocols; functional and molecular evaluation of neurogenetic disorders; translation of research data into medical protocols; new management perspectives; and multi-omic analysis of disease trajectory. This Special Issue of Genes will emphasize AI’s potential to bridge the gap between genetic research and clinical practice, offering a roadmap for future innovations in brain health. By leveraging AI’s computational power, precision medicine can move closer to individualized, data-driven solutions for treating neurological disorders, improving patient outcomes, and reshaping the future of neuroscience and healthcare.

Dr. Stefania Zampatti
Dr. Emiliano Giardina
Guest Editors

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Keywords

  • neurogenetics
  • genomics
  • artificial intelligence
  • brain disorders
  • neurodegenerative disease

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Published Papers (1 paper)

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Research

20 pages, 1417 KiB  
Article
Gene-Based Burden Testing of Rare Variants in Hemiplegic Migraine: A Computational Approach to Uncover the Genetic Architecture of a Rare Brain Disorder
by Mohammed M. Alfayyadh, Neven Maksemous, Heidi G. Sutherland, Rodney A. Lea and Lyn R. Griffiths
Genes 2025, 16(7), 807; https://doi.org/10.3390/genes16070807 - 9 Jul 2025
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Abstract
Background: HM is a rare, severe form of migraine with aura, characterised by motor weakness and strongly influenced by genetic factors affecting the brain. While pathogenic variants in CACNA1A, ATP1A2, and SCN1A genes have been implicated in familial HM, approximately 75% [...] Read more.
Background: HM is a rare, severe form of migraine with aura, characterised by motor weakness and strongly influenced by genetic factors affecting the brain. While pathogenic variants in CACNA1A, ATP1A2, and SCN1A genes have been implicated in familial HM, approximately 75% of cases lack known pathogenic variants in these genes, suggesting a more complex genetic basis. Methods: To advance our understanding of HM, we applied a variant prioritisation approach using whole-exome sequencing (WES) data from patients referred for HM diagnosis (n = 184) and utilised PathVar, a bioinformatics pipeline designed to identify pathogenic variants. Our analysis incorporated two strategies for association testing: (1) PathVar-identified single nucleotide variants (SNVs) and (2) PathVar SNVs combined with missense and rare variants. Principal component analysis (PCA) was performed to adjust for ancestral and other unknown differences between cases and controls. Results: Our results reveal a sequential reduction in the number of genes significantly associated with HM, from 20 in the first strategy to 11 in the second, which highlights the unique contribution of PathVar SNVs to the genetic architecture of HM. PathVar SNVs were more distinctive in the case cohort, suggesting a closer link to the functional changes underlying HM compared to controls. Notably, novel genes, such as SLC38A10, GCOM1, and NXPH2, which were previously not implicated in HM, are now associated with the disorder, advancing our understanding of its genetic basis. Conclusions: By prioritising PathVar SNVs, we identified a broader set of genes potentially contributing to HM. Given that HM is a rare condition, our findings, utilising a sample size of 184, represent a unique contribution to the field. This iterative analysis demonstrates that integrating diverse variant schemes provides a more comprehensive view of the genetic factors driving HM. Full article
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