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Forecasting autism gene discovery with machine learning and genome-scale data
[article]
2018
bioRxiv
pre-print
Genes are one of the most powerful windows into the biology of autism, and it has been estimated that perhaps a thousand or more genes may confer risk. However, less than 100 genes are currently viewed as having robust enough evidence to be considered true "autism genes". Massive genetic studies are underway to produce data to implicate additional genes, but this approach, although necessary, is costly and slow-moving. Here, we approach autism gene discovery as a machine learning problem,
doi:10.1101/370601
fatcat:fpznxidyanaptgi56svsw6ncqq