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Intellectual and Developmental Disabilities (IDDs), such as Down syndrome, Fragile X syndrome, Rett syndrome, and autism spectrum disorder, usually manifest at birth or early childhood. IDDs are characterized by significant impairment in intellectual and adaptive functioning, and both genetic and environmental factors underpin IDD biology. Molecular and genetic stratification of IDDs remain challenging mainly due to overlapping factors and comorbidity. Advances in high throughput sequencing, imaging, and tools to record behavioral data at scale have greatly enhanced our understanding of the molecular, cellular, structural, and environmental basis of some IDDs. Fueled by the “big data” revolution, artificial intelligence (AI) and machine learning (ML) technologies have brought a whole new paradigm shift in computational biology. Evidently, the ML-driven approach to clinical diagnoses has the potential to augment classical methods that use symptoms and external observations, hoping to push the personalized treatment plan forward. Therefore, integrative analyses and applications of ML technology have a direct bearing on discoveries in IDDs. The application of ML to IDDs can potentially improve screening and early diagnosis, advance our understanding of the complexity of comorbidity, and accelerate the identification of biomarkers for clinical research and drug development. For more than five decades, the IDDRC network has supported a nexus of investigators at centers across the USA, all striving to understand the interplay between various factors underlying IDDs. In this review, we introduced fast-increasing multi-modal data types, highlighted example studies that employed ML technologies to illuminate factors and biological mechanisms underlying IDDs, as well as recent advances in ML technologies and their applications to IDDs and other neurological diseases. We discussed various molecular, clinical, and environmental data collection modes, including genetic, imaging, phenotypical, and behavioral data types, along with multiple repositories that store and share such data. Furthermore, we outlined some fundamental concepts of machine learning algorithms and presented our opinion on specific gaps that will need to be filled to accomplish, for example, reliable implementation of ML-based diagnosis technology in IDD clinics. We anticipate that this review will guide researchers to formulate AI and ML-based approaches to investigate IDDs and related conditions.

 

ABSTRACT The E-isomer of cyanomethanimine (HNCHCN) was first identified in Sagittarius B2(N) (Sgr B2(N)) by a comparison of the publicly available Green Bank Telescope (GBT) PRIMOS survey with laboratory rotational spectra. Recently, Z-cyanomethanimine was detected in the quiescent molecular cloud G+0.693−0.027 with the IRAM 30-m telescope. Cyanomethanimine is a chemical intermediate in the proposed synthetic routes of adenine, and may play an important role in forming biological molecules in the interstellar medium. Here we present a new modelling study of cyanomethanimine, using the nautilus gas–grain reaction network and code with the addition of over 400 chemical reactions of the three cyanomethanimine isomers and related species. We apply cold isothermal core, hot core, and C-type shock models to simulate the complicated and heterogeneous physical environment in and in front of Sgr B2(N), and in G+0.693−0.027. We identify the major formation and destruction routes of cyanomethanimine, and find that the calculated abundances of the cyanomethanimine isomers and the ratio of Z-isomer to E-isomer are both in reasonable agreement with observations for selected environments. In particular, we conclude that these isomers are most likely formed within or near the hot core without the impact of shocks, or in the cold regions with shocks.