Title |
A review of heterogeneous data mining for brain disorder identification
|
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Published in |
Brain Informatics, September 2015
|
DOI | 10.1007/s40708-015-0021-3 |
Pubmed ID | |
Authors |
Bokai Cao, Xiangnan Kong, Philip S. Yu |
Abstract |
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, and multi-view feature analysis. In this paper, we review some recent data mining methods that are used for analyzing brain disorders. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Japan | 1 | 2% |
China | 1 | 2% |
Germany | 1 | 2% |
France | 1 | 2% |
Unknown | 54 | 93% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 12 | 21% |
Student > Bachelor | 10 | 17% |
Student > Master | 5 | 9% |
Student > Doctoral Student | 5 | 9% |
Researcher | 5 | 9% |
Other | 11 | 19% |
Unknown | 10 | 17% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 24 | 41% |
Engineering | 8 | 14% |
Biochemistry, Genetics and Molecular Biology | 3 | 5% |
Agricultural and Biological Sciences | 2 | 3% |
Environmental Science | 2 | 3% |
Other | 6 | 10% |
Unknown | 13 | 22% |