[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124617-en":3,"doc-seo-124617-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124617,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Identifying the neurodevelopmental and psychiatric signatures of genomic disorders associated with intellectual disability - a machine learning approach","Genomic conditions may present with developmental delay, intellectual disability, autism spectrum disorder, and physical and mental health symptoms, yet individual rarity and high variability limit standardized clinical guidance. A screening approach is needed to flag young people with neurodevelopmental genomic conditions who could benefit from further support. This study applies machine learning to clinician-assessed behavioural, neurodevelopmental, psychiatric, and physical health measures to distinguish cases from controls and to derive compact, discriminative sets of variables.","Donnelly et al. Molecular Autism (2023) 14:19 [https://doi.org/10.1186/s13229-023-00549-2](https://doi.org/10.1186/s13229-023-00549-2)  \nMolecular Autism  \n RESEARCH Open Access  \nIdentifying the neurodevelopmental  \nand psychiatric signatures of genomic disorders associated with intellectual disability: a machine learning approach  \nNicholas Donnelly1,2 , Adam Cunningham3 , Sergio Marco Salas3 , Matthew Bracher‑Smith3 , Samuel Chawner3 , Jan Stochl4,5 , Tamsin Ford4 , F. Lucy Raymond6 , Valentina Escott‑Price3 and Marianne B. M. van den Bree3*  \nAbstract  \nBackground Genomic conditions can be associated with developmental delay, intellectual disability, autism spec‑ trum disorder, and physical and mental health symptoms. They are individually rare and highly variable in presen‑ tation, which limits the use of standard clinical guidelines for diagnosis and treatment. A simple screening tool to identify young people with genomic conditions associated with neurodevelopmental disorders (ND‑GCs) who could benefit from further support would be of considerable value. We used machine learning approaches to address this question.  \nMethod A total of 493 individuals were included: 389 with a ND‑GC, mean age = 9. 01, 66% male) and 104 siblings without known genomic conditions (controls, mean age = 10. 23, 53% male) . Primary carers completed assessments of behavioural, neurodevelopmental and psychiatric symptoms and physical health and development. Machine learning techniques (penalised logistic regression, random forests, support vector machines and artificial neural networks) were used to develop classifiers of ND‑GC status and identified limited sets of variables that gave the best classifica‑ tion performance. Exploratory graph analysis was used to understand associations within the final variable set.  \nResults All machine learning methods identified variable sets giving high classification accuracy (AUROC between 0.883 and 0 . 915) . We identified a subset of 30 variables best discriminating between individuals with ND‑GCs and controls which formed 5 dimensions: conduct, separation anxiety, situational anxiety, communication and motor development.  \nLimitations This study used cross‑sectional data from a cohort study which was imbalanced with respect to ND‑GC status. Our model requires validation in independent datasets and with longitudinal follow‑up data for validation before clinical application.  \nConclusions In this study, we developed models that identified a compact set of psychiatric and physical health measures that differentiate individuals with a ND‑GC from controls and highlight higher‑order structure within these  \n*Correspondence:  \nMarianne B. M. van den Bree  \n[vandenBreeMB@cardiff.ac.uk](vandenBreeMB@cardiff.ac.uk)  \nFull list of author information is available at the end of the article  \n© The Author(s) 2023. Open Access This 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, visit [http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ()[. The Creative Commons Public Domain Dedication waiver (](http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ()[http://creati","cbCaisTbPKOEED3q","https://ap.wps.com/l/cbCaisTbPKOEED3q","pdf",2376340,1,13,"English","en",105,"# Abstract\n## Background\n## Method\n## Results\n## Limitations\n## Conclusions\n# Background\n## Prevalence and types of genomic conditions\n## Clinical variability and specific examples","[{\"question\":\"Why is a screening tool for neurodevelopmental genomic conditions needed?\",\"answer\":\"Genomic conditions are individually rare and highly variable, which restricts the use of standard clinical guidelines. A screening tool could identify young people who would benefit from further specialized support.\"},{\"question\":\"How was the machine learning approach performed in the study?\",\"answer\":\"Assessments by primary carers captured behavioural, neurodevelopmental, psychiatric symptoms, and physical health/development. Multiple machine learning models (penalised logistic regression, random forests, support vector machines, and artificial neural networks) were used to classify ND-GC status and select variables with strong performance.\"},{\"question\":\"What dimensions and variables best distinguished ND-GCs from controls?\",\"answer\":\"All models achieved high classification accuracy (AUROC 0.883–0.915). A subset of 30 variables formed five dimensions: conduct, separation anxiety, situational anxiety, communication, and motor development.\"}]","Identifying the neurodevelopmental and psychiatric signatures of genomic disorders associated with intellectual disability - a machine learning approach | PDF",1785893342,33,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"identifying-the-neurodevelopmental-and-psychiatric-signatures-of-genomic-disorders-associated-with-intellectual-disability-a-machine-learning-approach-124617","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/identifying-the-neurodevelopmental-and-psychiatric-signatures-of-genomic-disorders-associated-with-intellectual-disability-a-machine-learning-approach-124617/124617/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is a screening tool for neurodevelopmental genomic conditions needed?","Question",{"text":75,"@type":76},"Genomic conditions are individually rare and highly variable, which restricts the use of standard clinical guidelines. A screening tool could identify young people who would benefit from further specialized support.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the machine learning approach performed in the study?",{"text":80,"@type":76},"Assessments by primary carers captured behavioural, neurodevelopmental, psychiatric symptoms, and physical health/development. Multiple machine learning models (penalised logistic regression, random forests, support vector machines, and artificial neural networks) were used to classify ND-GC status and select variables with strong performance.",{"name":82,"@type":73,"acceptedAnswer":83},"What dimensions and variables best distinguished ND-GCs from controls?",{"text":84,"@type":76},"All models achieved high classification accuracy (AUROC 0.883–0.915). A subset of 30 variables formed five dimensions: conduct, separation anxiety, situational anxiety, communication, and motor development.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]