Title |
Random forests on Hadoop for genome-wide association studies of multivariate neuroimaging phenotypes
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Published in |
BMC Bioinformatics, October 2013
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DOI | 10.1186/1471-2105-14-s16-s6 |
Pubmed ID | |
Authors |
Yue Wang, Wilson Goh, Limsoon Wong, Giovanni Montana, the Alzheimer's Disease Neuroimaging Initiative |
Abstract |
Multivariate quantitative traits arise naturally in recent neuroimaging genetics studies, in which both structural and functional variability of the human brain is measured non-invasively through techniques such as magnetic resonance imaging (MRI). There is growing interest in detecting genetic variants associated with such multivariate traits, especially in genome-wide studies. Random forests (RFs) classifiers, which are ensembles of decision trees, are amongst the best performing machine learning algorithms and have been successfully employed for the prioritisation of genetic variants in case-control studies. RFs can also be applied to produce gene rankings in association studies with multivariate quantitative traits, and to estimate genetic similarities measures that are predictive of the trait. However, in studies involving hundreds of thousands of SNPs and high-dimensional traits, a very large ensemble of trees must be inferred from the data in order to obtain reliable rankings, which makes the application of these algorithms computationally prohibitive. |
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Geographical breakdown
Country | Count | As % |
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United States | 2 | 50% |
Mexico | 1 | 25% |
Unknown | 1 | 25% |
Demographic breakdown
Type | Count | As % |
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Scientists | 2 | 50% |
Members of the public | 2 | 50% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United States | 2 | 2% |
Switzerland | 1 | <1% |
Germany | 1 | <1% |
Singapore | 1 | <1% |
Brazil | 1 | <1% |
Unknown | 121 | 95% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 34 | 27% |
Student > Master | 20 | 16% |
Researcher | 17 | 13% |
Professor > Associate Professor | 8 | 6% |
Student > Bachelor | 6 | 5% |
Other | 20 | 16% |
Unknown | 22 | 17% |
Readers by discipline | Count | As % |
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Computer Science | 24 | 19% |
Agricultural and Biological Sciences | 23 | 18% |
Medicine and Dentistry | 15 | 12% |
Biochemistry, Genetics and Molecular Biology | 10 | 8% |
Neuroscience | 6 | 5% |
Other | 21 | 17% |
Unknown | 28 | 22% |