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A network clustering based feature selection strategy for classifying autism spectrum disorder
2019
BMC Medical Genomics
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Advanced non-invasive neuroimaging techniques offer new approaches to study functions and structures of human brains. Whole-brain functional networks obtained from resting state functional magnetic resonance imaging has been widely used to study brain diseases like autism spectrum disorder (ASD). Auto-classification of ASD has become an important issue. Existing classification methods for ASD are based on features extracted from the whole-brain functional networks, which may be not discriminant enough for good performance.
doi:10.1186/s12920-019-0598-0
pmid:31888621
pmcid:PMC6936069
fatcat:ikbvwjpdkbeshblsmtsf27coey