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Non-negative matrix factorization of multimodal MRI, fMRI and phenotypic data reveals differential changes in default mode subnetworks in ADHD.

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Using insurance claims to predict and improve hospitalizations and biologics use in members with inflammatory bowel diseases., , , , , , and . Journal of Biomedical Informatics, (2018)Balancing Clinical and Pathologic Relevance in the Machine Learning Diagnosis of Epilepsy., , , , , , , , , and 2 other author(s). PRNI, page 86-89. IEEE, (2013)Parameter Selection in Mutual Information-Based Feature Selection in Automated Diagnosis of Multiple Epilepsies Using Scalp EEG., , , , , , and . PRNI, page 45-48. IEEE Computer Society, (2012)Electronic health record phenotyping improves detection and screening of type 2 diabetes in the general United States population: A cross-sectional, unselected, retrospective study., , , , , and . Journal of Biomedical Informatics, (2016)Cross-diagnostic Prediction of Dimensional Psychiatric Phenotypes in Anorexia Nervosa and Body Dysmorphic Disorder Using Multimodal Neuroimaging and Psychometric Data., , , , , , , and . GRAIL/Beyond-MIC@MICCAI, volume 11044 of Lecture Notes in Computer Science, page 92-99. Springer, (2018)Non-negative matrix factorization of multimodal MRI, fMRI and phenotypic data reveals differential changes in default mode subnetworks in ADHD., , , , , , , , and . NeuroImage, (2014)Multimodal diagnosis of epilepsy using conditional dependence and multiple imputation., , , , , , , , , and 8 other author(s). PRNI, page 1-4. IEEE, (2014)The utility of data-driven feature selection: Re: Chu et al. 2012., , , and . NeuroImage, (2014)