2015/08/05 by Bokai Cao, Cao, Bokai, Xiangnan Kong +3
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · Medicine · Neuroscience · Psychology · #Advanced Neuroimaging Techniques and Applications #Applications (stat.AP) #Artificial intelligence #Big data #Computational Engineering #Computer science #Data mining #Data science #Data sharing #Databases (cs.DB) #FOS: Biological sciences #FOS: Computer and information sciences #Finance #Functional Brain Connectivity Studies #Identification (biology) #Machine Learning (cs.LG) #Machine learning #Medicine #Neuroimaging #Neurons and Cognition (q-bio.NC) #Neuroscience #Psychology #Raw data #Tensor decomposition and applications #Transformative learning #and Science (cs.CE) #cs.CE #cs.DB #cs.LG #q-bio.NC #stat.AP
paper · pdf · doi:10.48550/arxiv.1508.01023
arxiv created 2015/08/05 · openalex publication_date 2015/08/05 · arxiv updated 2015/08/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04
With rapid advances in neuroimaging techniques, the research on brain disorder identification has become an emerging area in the data mining community. Brain disorder data poses many unique challenges for data mining research. For example, the raw data generated by neuroimaging experiments is in tensor representations, with typical characteristics of high dimensionality, structural complexity and nonlinear separability. Furthermore, brain connectivity networks can be constructed from the tensor data, embedding subtle interactions between brain regions. Other clinical measures are usually available reflecting the disease status from different perspectives. It is expected that integrating complementary information in the tensor data and the brain network data, and incorporating other clinical parameters will be potentially transformative for investigating disease mechanisms and for informing therapeutic interventions. Many research efforts have been devoted to this area. They have achieved great success in various applications, such as tensor-based modeling, subgraph pattern mining, multi-view feature analysis. In this paper, we review some recent data mining methods that are used for analyzing brain disorders.