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

Table 2 Published research articles demonstrating machine learning applications to intellectual and developmental disabilities

Reference	Disorder (in order of reference)	Data type	Application
Single-view learning
Koivu et al. (2018) [123]	Down’s Syndrome (DS)	NeuroImage	Disease risk assessment
Tenev et al. (2014) [119], Chen et al. (2019) [118], Eslami et al. (2019) [120]	ADHD, ADHD, ASD	NeuroImage	Patient classification
Wang & Avillach (2021) [130], Feng et al. (2018) [126], Liu et al. (2021) [129]	ASD, DS, ADHD	Multi-Omics, Behavior	Patient classification
Hazlett et al. (2017) [115], Heinsfeld et al. (2018) [116]	ASD	NeuroImage	Diagnosis
Heinsfeld et al. (2018) [116], Movaghar et al. (2021) [103]	ASD, FXS	Clinical	Diagnosis
Stahl et al. (2012) [122]	Cerebral Palsy (CP)	Behavior	Diagnosis
Ramaswami et al. (2020) [21]	ASD	Multi-Omics	Disease sub-typing
Voineagu (2011) [157], Johnson et al. (2016) [158], Gupta et al. (2014) [22]	ASD, Neurodevelopmental disease, ASD	Multi-Omics	Biomarker discovery
Liu et al. (2021) [129]	ADHD	Multi-Omics	Biomarker discovery
Cogill et al. (2016) [125]	ASD	Multi-Omics	Gene prioritization
Kimura et al. (2019) [159]	Williams syndrome	Multi-Omics	Cellular/molecular pathways
Multi-view learning
Colby et al. (2012) [138], Libero et al. (2015) [139]	ADHD, ASD	NeuroImage	Patient classification
Jacobs et al. (2021) [165]	Multiple	NeuroImage, Behavior	Disease sub-typing

ISSN: 1866-1955