Search

In the big data era, artificial intelligence techniques have been applied to tackle traditional issues in the study of neurodegenerative diseases. Despite the progress made in understanding the complex (epi)genetics signatures underlying neurodegenerative disorders, performing early diagnosis and developing drug repurposing strategies remain serious challenges for such conditions. In this context, the integration of multi-omics, neuroimaging, and electronic health records data can be exploited using deep learning methods to provide the most accurate representation of patients possible. Deep learning allows researchers to find multi-modal biomarkers to develop more effective and personalized treatments, early diagnosis tools, as well as useful information for drug discovering and repurposing in neurodegenerative pathologies. In this review, we will describe how relevant studies have been able to demonstrate the potential of deep learning to enhance the knowledge of neurodegenerative disorders such as Alzheimer’s and Parkinson’s diseases through the integration of all sources of biomedical data.

This work describes the application of NGS for molecular diagnosis of RP in a family with a history of severe hypovision. In particular, the proband received a clinical diagnosis of RP on the basis of medical, instrumental examinations and his family history. The proband was subjected to NGS, utilizing a customized panel including 24 genes associated with RP and other retinal dystrophies. The NGS analysis revealed a novel missense variant (c.668T &gt, A, I223N) in PRPH2 gene, which was investigated by segregation and bioinformatic analysis. The variant is located in the D2 loop domain of PRPH2, which is critical for protein activity. Bioinformatic analysis described the c.668T &gt, A as a likely pathogenic variant. Moreover, a 3D model prediction was performed to better characterize the impact of the variant on the protein, reporting a disruption of the &alpha, helical structures. As a result, the variant protein showed a substantially different conformation with respect to the wild-type PRPH2. The identified variant may therefore affect the oligomerization ability of the D2 loop and, ultimately, hamper PRPH2 proper functioning and localization. In conclusion, PRPH2_c.668T &gt, A provided a molecular explanation of RP symptomatology, highlighting the clinical utility of NGS panels to facilitate genotype&ndash, phenotype correlations.