Title |
Data mining of high density genomic variant data for prediction of Alzheimer's disease risk
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Published in |
BMC Medical Genomics, January 2012
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DOI | 10.1186/1471-2350-13-7 |
Pubmed ID | |
Authors |
Natalia Briones, Valentin Dinu |
Abstract |
The discovery of genetic associations is an important factor in the understanding of human illness to derive disease pathways. Identifying multiple interacting genetic mutations associated with disease remains challenging in studying the etiology of complex diseases. And although recently new single nucleotide polymorphisms (SNPs) at genes implicated in immune response, cholesterol/lipid metabolism, and cell membrane processes have been confirmed by genome-wide association studies (GWAS) to be associated with late-onset Alzheimer's disease (LOAD), a percentage of AD heritability continues to be unexplained. We try to find other genetic variants that may influence LOAD risk utilizing data mining methods. |
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Mendeley readers
Geographical breakdown
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Demographic breakdown
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Student > Ph. D. Student | 14 | 20% |
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Professor | 4 | 6% |
Student > Bachelor | 4 | 6% |
Other | 8 | 11% |
Unknown | 8 | 11% |
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Neuroscience | 6 | 9% |
Other | 15 | 21% |
Unknown | 9 | 13% |