Successful classification of cocaine dependence using brain imaging: a generalizable machine learning approach

Background Neuroimaging studies have yielded significant advances in the understanding of neural processes relevant to the development and persistence of addiction. However, these advances have not explored extensively for diagnostic accuracy in human subjects. The aim of this study was to develop a...

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Veröffentlicht in:	BMC bioinformatics Jg. 17; H. Suppl 13; S. 357
Hauptverfasser:	Mete, Mutlu, Sakoglu, Unal, Spence, Jeffrey S., Devous, Michael D., Harris, Thomas S., Adinoff, Bryon
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 06.10.2016 Springer Nature B.V
Schlagworte:	Adult Algorithms Bioinformatics Biomedical and Life Sciences Brain Brain - diagnostic imaging Brain - pathology Classification Cluster Analysis Cocaine Cocaine-Related Disorders - classification Cocaine-Related Disorders - diagnostic imaging Cocaine-Related Disorders - pathology Computational Biology/Bioinformatics Computer Appl. in Life Sciences Drug abuse Female Humans Life Sciences Male Microarrays Middle Aged Neuroimaging - methods Proceedings Sensitivity and Specificity Statistical methods Substance use Support Vector Machine Young Adult Substance use disorders Cocaine dependence Support vector machines Machine learning Classification
ISSN:	1471-2105, 1471-2105
Online-Zugang:	Volltext
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