A review of heterogeneous data mining for brain disorder identification

Brain Informatics

Volumn 2, Issue 4, 2015, Pages 253-264

  Cao, Bokai ^a   Kong, Xiangnan ^b   Yu, Philip S ^a,c  

^a University of Illinois at Chicago   (United States)

^b Worcester Polytechnic Institute   (United States)

^c TSINGHUA UNIVERSITY   (China)

Author keywords

Brain diseases; Data mining; Feature selection; Subgraph patterns; Tensor analysis

Indexed keywords

BRAIN; FEATURE EXTRACTION; NEUROIMAGING; TENSORS;

BRAIN DISEASE; HETEROGENEOUS DATA MININGS; NEUROIMAGING TECHNIQUES; NONLINEAR SEPARABILITY; SUBGRAPHS; TENSOR ANALYSIS; TENSOR REPRESENTATION; THERAPEUTIC INTERVENTION;

DATA MINING;

ALZHEIMER DISEASE; BIOINFORMATICS; BRAIN ATROPHY; BRAIN DISEASE; CONNECTOME; DATA MINING; DEFAULT MODE NETWORK; DIFFUSION TENSOR IMAGING; ELECTROCORTICOGRAPHY; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HUMAN; LEARNING; LEARNING ALGORITHM; MATHEMATICAL MODEL; MILD COGNITIVE IMPAIRMENT; NEUROIMAGING; PRIORITY JOURNAL; REVIEW;

EID: 85027875638     PISSN: 21984018     EISSN: 21984026     Source Type: Journal    
DOI: 10.1007/s40708-015-0021-3     Document Type: Review

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