T253. A Machine Learning Approach to Predict the Changes of Brain Functional Connectivity in Autism Spectrum Disorder From the Gene Expression Data

AbstractMotivationAutism spectrum disorder (ASD) has a strong, yet heterogeneous, genetic component. Among the various methods that are being developed to help reveal the underlying molecular aetiology of the disease, one that is gaining popularity is the combination of gene expression and clinical genetic data. For ASD, the SFARI-gene database comprises lists of curated genes in which presumed causative mutations have been identified in patients. In order to predict novel candidate SFARI-genes we built classification models combining differential gene expression data for ASD patients and unaffected individuals with a gene’s status in the SFARI-gene list.ResultsSFARI-genes were not found to be significantly associated with differential gene expression patterns, nor were they enriched in gene co-expression network modules that had a strong correlation with ASD diagnosis. However, network analysis and machine learning models that incorporate information from the whole gene co-expression network were able to predict novel candidate genes that share features of existing SFARI genes and have support for roles in ASD in the literature. We found a statistically significant bias related to the absolute level of gene expression for existing SFARI genes and their scores. It is essential that this bias be taken into account when studies interpret ASD gene expression data at gene, module and whole-network levels.AvailabilitySource code is available from GitHub (https://doi.org/10.5281/zenodo.4463693) and the accompanying data from The University of Edinburgh DataStore (https://doi.org/10.7488/ds/2980)[email protected]

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Risk Assessment for Parents Who Suspect Their Child Has Autism Spectrum Disorder: Machine Learning Approach

Journal of Medical Internet Research ◽

10.2196/jmir.9496 ◽

2018 ◽

Vol 20 (4) ◽

pp. e134 ◽

Cited By ~ 7

Author(s):

Ayelet Ben-Sasson ◽

Diana L Robins ◽

Elad Yom-Tov

Keyword(s):

Machine Learning ◽

Risk Assessment ◽

Autism Spectrum Disorder ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Learning Approach ◽

Machine Learning Approach

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Machine learning approach to identify resting-state functional connectivity pattern serving as an endophenotype of autism spectrum disorder

10.1101/348599 ◽

2018 ◽

Author(s):

Bun Yamagata ◽

Takashi Itahashi ◽

Junya Fujino ◽

Haruhisa Ohta ◽

Motoaki Nakamura ◽

...

Keyword(s):

Machine Learning ◽

Autism Spectrum Disorder ◽

Resting State ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Connectivity Pattern ◽

Learning Approach ◽

Functional Connections ◽

Communication Impairment ◽

Machine Learning Approach

AbstractEndophenotype refers to a measurable and heritable component between genetics and diagnosis and exists in both individuals with a diagnosis and their unaffected siblings. We aimed to identify a pattern of endophenotype consisted of multiple connections. We enrolled adult male individuals with autism spectrum disorder (ASD) endophenotype (i.e., individuals with ASD and their unaffected siblings) and individuals without ASD endophenotype (i.e., pairs of typical development (TD) siblings) and utilized a machine learning approach to classify people with and without endophenotypes, based on resting-state functional connections (FCs). A sparse logistic regression successfully classified people as to the endophenotype (area under the curve=0.78, classification accuracy=75%), suggesting the existence of endophenotype pattern. A binomial test identified that nine FCs were consistently selected as inputs for the classifier. The least absolute shrinkage and selection operator with these nine FCs predicted severity of communication impairment among individuals with ASD (r=0.68, p=0.021). In addition, two of the nine FCs were statistically significantly correlated with the severity of communication impairment (r=0.81, p=0.0026 and r=-0.60, p=0.049). The current findings suggest that an ASD endophenotype pattern exists in FCs with a multivariate manner and is associated with clinical ASD phenotype.

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