Recently, genome-wide association studies have substantially expanded our knowledge about genetic variants that influence the susceptibility to complex diseases. Although standard statistical tests for each single-nucleotide polymorphism (SNP) separately are able to capture main genetic effects, dif
β¦ LIBER β¦
Predicting genome-wide redundancy using machine learning
β Scribed by Huang-Wen Chen; Sunayan Bandyopadhyay; Dennis E Shasha; Kenneth D Birnbaum
- Book ID
- 115001836
- Publisher
- BioMed Central
- Year
- 2010
- Tongue
- English
- Weight
- 657 KB
- Volume
- 10
- Category
- Article
- ISSN
- 1471-2148
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## Abstract Over the last few years, many new genetic associations have been identified by genomeβwide association studies (GWAS). There are potentially many uses of these identified variants: a better understanding of disease etiology, personalized medicine, new leads for studying underlying biolo
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