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A Validated Phenotyping Algorithm for Genetic Association Studies in Age-related Macular Degeneration.
Simonett JM, Sohrab MA, Pacheco J, Armstrong LL, Rzhetskaya M, Smith M, Geoffrey Hayes M, Fawzi AA. Simonett JM, et al. Sci Rep. 2015 Aug 10;5:12875. doi: 10.1038/srep12875. Sci Rep. 2015. PMID: 26255974 Free PMC article.
Age-related macular degeneration (AMD), a multifactorial, neurodegenerative disease, is a leading cause of vision loss. With the rapid advancement of DNA sequencing technologies, many AMD-associated genetic polymorphisms have been identified. ..
Age-related macular degeneration (AMD), a multifactorial, neurodegenerative disease, is a leading cause of visio
Genotype distribution-based inference of collective effects in genome-wide association studies: insights to age-related macular degeneration disease mechanism.
Woo HJ, Yu C, Kumar K, Gold B, Reifman J. Woo HJ, et al. BMC Genomics. 2016 Aug 30;17(1):695. doi: 10.1186/s12864-016-2871-3. BMC Genomics. 2016. PMID: 27576376 Free PMC article.
BACKGROUND: Genome-wide association studies provide important insights to the genetic component of disease risks. However, an existing challenge is how to incorporate collective effects of interactions beyond the level of independent single nucleotide polymor …
BACKGROUND: Genome-wide association studies provide important insights to the genetic component of disease risks. Howev …
Chances and challenges of machine learning-based disease classification in genetic association studies illustrated on age-related macular degeneration.
Guenther F, Brandl C, Winkler TW, Wanner V, Stark K, Kuechenhoff H, Heid IM. Guenther F, et al. Genet Epidemiol. 2020 Aug 2. doi: 10.1002/gepi.22336. Online ahead of print. Genet Epidemiol. 2020. PMID: 32741009
Imaging technology and machine learning algorithms for disease classification set the stage for high-throughput phenotyping and promising new avenues for genome-wide association studies (GWAS). ...To evaluate chances and challenges, we performed a GWAS …
Imaging technology and machine learning algorithms for disease classification set the stage for high-throughput phenotyping an …
Identification of multiple gene-gene interactions for ordinal phenotypes.
Kim K, Kwon MS, Oh S, Park T. Kim K, et al. BMC Med Genomics. 2013;6 Suppl 2(Suppl 2):S9. doi: 10.1186/1755-8794-6-S2-S9. Epub 2013 May 7. BMC Med Genomics. 2013. PMID: 23819572 Free PMC article.
BACKGROUND: Multifactor dimensionality reduction (MDR) is a powerful method for analysis of gene-gene interactions and has been successfully applied to many genetic studies of complex diseases. ...Also, we generalized cross-validation consistency (GCVC) to id …
BACKGROUND: Multifactor dimensionality reduction (MDR) is a powerful method for analysis of gene-gene interactions and has been successfully …
Identifying and mitigating batch effects in whole genome sequencing data.
Tom JA, Reeder J, Forrest WF, Graham RR, Hunkapiller J, Behrens TW, Bhangale TR. Tom JA, et al. BMC Bioinformatics. 2017 Jul 24;18(1):351. doi: 10.1186/s12859-017-1756-z. BMC Bioinformatics. 2017. PMID: 28738841 Free PMC article.
No systematic algorithms or heuristics exist to detect and filter batch effects or remove associations impacted by batch effects in whole genome sequencing data. ...We developed and validated methods and filters to address this deficiency....
No systematic algorithms or heuristics exist to detect and filter batch effects or remove associations impacted by batch effects in w …
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