Updates from the Past Few Months
The Wall Lab has been very busy these past few months with four new papers published:
First, “Outgroup Machine Learning Approach Identifies Single Nucleotide Variants in Noncoding DNA Associated with Autism Spectrum Disorder”, was published to the Pacific Symposium on Biocomputing. This paper investigates the role of noncoding variation in the ASD phenotype and shows the importance of the noncoding region and the utility of independent control groups in effectively linking genetic variation to disease phenotype for complex disorders through whole genome sequencing and machine learning models.
Next, “Detecting Developmental Delay and Autism Through Machine Learning Models Using Home Videos of Bangladeshi Children: Development and Validation Study” was published to the Journal of Medical Internet Research. This paper is a continuation of our previous work, in which we demonstrated the efficacy of machine learning classifiers to accelerate the process by collecting home videos of US-based children, identifying a reduced subset of behavioral features that are scored by untrained raters using a machine learning classifier to determine children’s “risk scores” for autism. Using videos of Bangladeshi children collected from Dhaka Shishu Children’s Hospital, we aim to scale our pipeline to another culture and other developmental delays, including speech and language conditions.
Another paper, entitled “Validity of Online Screening for Autism: Crowdsourcing Study Comparing Paid and Unpaid Diagnostic Tasks”, was published to the Journal of Medical Internet Research. This paper performed a series of studies to explore whether paid crowd workers on Amazon Mechanical Turk (AMT) and citizen crowd workers on a public website shared on social media can provide accurate online detection of autism, conducted via crowdsourced ratings of short home video clips.
And lastly, “Identification and Quantification of Gaps in Access to Autism Resources in the United States: An Infodemiological Study”, was published to the Journal of Medical Internet Research. Using the GapMap database, this paper quantifies the gaps in access to autism resources through computing the average distance between the nearest resource and individual with ASD as well as the relative disconnect between supply and demand of autism diagnostic resources across the U.S.
Check out these published papers below:
Outgroup Machine Learning Approach Identifies Single Nucleotide Variants in Noncoding DNA Associated with Autism Spectrum Disorder
Detecting Developmental Delay and Autism Through Machine Learning Models Using Home Videos of Bangladeshi Children: Development and Validation Study
Validity of Online Screening for Autism: Crowdsourcing Study Comparing Paid and Unpaid Diagnostic Tasks
Identification and Quantification of Gaps in Access to Autism Resources in the United States: An Infodemiological Study
Check back soon for more exciting updates!