In this review, we discuss the fundamental machinery of AI algorithms (deep neural network and evolutionary algorithms) that can potentially have significant impact resolving problems related to broader neurodevelopmental disorders. We have discussed in depth the key unresolved issues in NDD and how AI can impact by modeling solutions related to new gene discovery, gene-gene interactions, phenotypic-genotypic heterogeneity, and drug discovery. We have discussed how artificial intelligence alongside with genomics can be at the center of precision medicine solutions for neurodevelopmental disorders. This paper discussed the application of AI within the context of neurodevelopmental disorders complexities. We have pointed out what is feasible for AI to achieve and the challenges that is far too complex to implement. We have portrait some scenarios where integrating AI with other emerging cutting edge technologies (i.e. CRISPR/Cas9, brain organoid, antisense therapy) might create massive impact in neurodevelopmental disorders diagnosis and therapeutics.
Globally, reports have shown that the COVID-19 Omicron variant results in less severe disease compared to previous variants, including Delta. The extent to which disease severity differs based on vaccination status and in high-risk populations is poorly characterized.
ALS is a famous neurodegenerative disease. Misfolded SOD1 has been linked to both familial and sporadic ALS. Here, we present a cryo-EM structure of SOD1 fibrils, providing insights into the conversion of SOD1 from its immature form into an aggregated form during pathogenesis of ALS.
A prompt response to a pandemic requires data from different sources to be merged and analyzed in the most efficient way. Our work describes how we approached this challenge for COVID-19 data within the European Project ORCHESTRA
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