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Bringing machine learning to research on intellectual and developmental disabilities: taking inspiration from neurological diseases

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 en...

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Autores principales: Gupta, Chirag, Chandrashekar, Pramod, Jin, Ting, He, Chenfeng, Khullar, Saniya, Chang, Qiang, Wang, Daifeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9059371/
https://www.ncbi.nlm.nih.gov/pubmed/35501679
http://dx.doi.org/10.1186/s11689-022-09438-w
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author Gupta, Chirag
Chandrashekar, Pramod
Jin, Ting
He, Chenfeng
Khullar, Saniya
Chang, Qiang
Wang, Daifeng
author_facet Gupta, Chirag
Chandrashekar, Pramod
Jin, Ting
He, Chenfeng
Khullar, Saniya
Chang, Qiang
Wang, Daifeng
author_sort Gupta, Chirag
collection PubMed
description 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.
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spelling pubmed-90593712022-05-03 Bringing machine learning to research on intellectual and developmental disabilities: taking inspiration from neurological diseases Gupta, Chirag Chandrashekar, Pramod Jin, Ting He, Chenfeng Khullar, Saniya Chang, Qiang Wang, Daifeng J Neurodev Disord Review 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. BioMed Central 2022-05-02 /pmc/articles/PMC9059371/ /pubmed/35501679 http://dx.doi.org/10.1186/s11689-022-09438-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visithttp://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Review
Gupta, Chirag
Chandrashekar, Pramod
Jin, Ting
He, Chenfeng
Khullar, Saniya
Chang, Qiang
Wang, Daifeng
Bringing machine learning to research on intellectual and developmental disabilities: taking inspiration from neurological diseases
title Bringing machine learning to research on intellectual and developmental disabilities: taking inspiration from neurological diseases
title_full Bringing machine learning to research on intellectual and developmental disabilities: taking inspiration from neurological diseases
title_fullStr Bringing machine learning to research on intellectual and developmental disabilities: taking inspiration from neurological diseases
title_full_unstemmed Bringing machine learning to research on intellectual and developmental disabilities: taking inspiration from neurological diseases
title_short Bringing machine learning to research on intellectual and developmental disabilities: taking inspiration from neurological diseases
title_sort bringing machine learning to research on intellectual and developmental disabilities: taking inspiration from neurological diseases
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9059371/
https://www.ncbi.nlm.nih.gov/pubmed/35501679
http://dx.doi.org/10.1186/s11689-022-09438-w
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