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Bayesian Multiresolution Variable Selection for Ultra-High Dimensional Neuroimaging Data
Ultra-high dimensional variable selection has become increasingly important in analysis of neuroimaging data. For example, in the Autism Brain Imaging Data Exchange (ABIDE) study, neuroscientists are interested in identifying important biomarkers for early detection of the autism spectrum disorder (...
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| 出版年: | IEEE/ACM Trans Comput Biol Bioinform |
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| 主要な著者: | , , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
2018
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| 主題: | |
| オンライン・アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5885321/ https://ncbi.nlm.nih.gov/pubmed/29610102 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1109/TCBB.2015.2440244 |
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