A pilot study on machine learning approach to delineate metabolic signatures in intellectual disability.
Intellectual disability (ID) is a neurodevelopmental disorder characterized by cognitive delays. Inborn errors of metabolism constitute an important subgroup of ID for which various treatments options are available. We aimed to identify potential biomarkers of inherited metabolic disorders from the...
| Publicado en: | International Journal of Developmental Disabilities Vol. 67; no. 2; pp. 94 - 101 |
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| Autores principales: | , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Apr2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=149790081&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 149790081 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20473869 F2SP jtl: International Journal of Developmental Disabilities issn: 20473869 maglogo: N pubinfo: dt: Apr2021 vid: 67 iid: 2 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 149790081 149790081 149790081 10.1080/20473869.2019.1599168 149790081 ppf: 94 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A pilot study on machine learning approach to delineate metabolic signatures in intellectual disability. aug: au: Nikam, Vidya Ranade, Suvidya Shaik Mohammad, Naushad Kulkarni, Mohan affil: Department of Chemistry, Biochemistry Division, Savitribai Phule Pune University, Pune, India sug: subj: Intellectual Disability In Infancy and Childhood Machine Learning Metabolism, Inborn Errors Biological Markers Blood Human Descriptive Statistics Pilot Studies Mass Spectrometry Methods Algorithms Child Child: 6-12 years ab: Intellectual disability (ID) is a neurodevelopmental disorder characterized by cognitive delays. Inborn errors of metabolism constitute an important subgroup of ID for which various treatments options are available. We aimed to identify potential biomarkers of inherited metabolic disorders from the children with ID using tandem mass spectrometry and develop a novel machine learning algorithm to differentiate between the cases and the controls. All of the cases were having IQ score <70, gross motor delay, speech disorder and no recognizable symptoms of the condition. Metabolite profiling of ID individuals exhibited low tyrosine/large neutral amino acids, high citrulline/arginine ratios; elevated proline, alanine, phenylalanine, and ornithine, while a significant decrease in the level of amino acid arginine, and elevated C4 (butyrylcarnitine) and C4OH/C3DC (3-hydroxybutyrylcarnitine/malonylcarnitine). Machine learning algorithm differentiated cases and controls efficiently using specific thresholds of ornithine, arginine and C4OH/C3DC. Furthermore, ID cases were distinguished into mild, moderate, and severe based on specific thresholds of methionine, arginine, and C5OH/C4DC (3-hydroxyisovalerylcarnitine/methylmalonylcarnitine). The machine learning algorithm could successfully identify specific metabolite markers in ID and correlate the same with neurological features. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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