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• W2996936198 abstract "Abstract Background 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. Methods In this study, we propose a network clustering based feature selection strategy for classifying ASD. In our proposed method, we first apply symmetric non-negative matrix factorization to divide brain networks into four modules. Then we extract features from one of four modules called default mode network (DMN) and use them to train several classifiers for ASD classification. Results The computational experiments show that our proposed method achieves better performances than those trained with features extracted from the whole brain network. Conclusion It is a good strategy to train the classifiers for ASD based on features from the default mode subnetwork." @default.
• W2996936198 created "2020-01-10" @default.
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• W2996936198 date "2019-12-01" @default.
• W2996936198 modified "2023-09-23" @default.
• W2996936198 title "A network clustering based feature selection strategy for classifying autism spectrum disorder" @default.
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• W2996936198 doi "https://doi.org/10.1186/s12920-019-0598-0" @default.
• W2996936198 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6936069" @default.
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• W2996936198 hasPublicationYear "2019" @default.
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