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Andrew D Paterson

Showing results (21-30 of 276) with videos related to

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BMC Genetics|February 3, 2006
Evaluating outlier loci and their effect on the identification of pedigree errorsKe-Sheng Wang, Michelle Liu, Andrew D Paterson
Biological Psychiatry|July 18, 2008
Genome-wide linkage analyses of quantitative and categorical autism subphenotypesXiao-Qing Liu, Andrew D Paterson, Peter Szatmari, et al.
BMC Proceedings|December 17, 2016
Factors associated with heterogeneity in microarray gene expression in peripheral blood mononuclear cells from large pedigreesMichael Gallaugher, Angelo J Canty, Andrew D Paterson
Plos One|July 7, 2015
Assessment and Selection of Competing Models for Zero-Inflated Microbiome DataLizhen Xu, Andrew D Paterson, Williams Turpin, et al.
Canadian Journal of Diabetes|March 9, 2026
Insulin response to glucose and type 2 diabetes and its impact on cardiometabolic disease: a sex-stratified Mendelian randomization study to assess potential causalityYuchao Wu, Habiba Hashemy, Andrew D Paterson, et al.
BMC Proceedings|December 19, 2014
Genetic Analysis Workshop 18 single-nucleotide variant prioritization based on protein impact, sequence conservation, and gene annotationThomas Nalpathamkalam, Andriy Derkach, Andrew D Paterson, et al.
BMC Proceedings|May 10, 2008
The multiplicity problem in linkage analysis of gene expression data - the power of differentiating cis- and trans-acting regulatorsBaisong Huang, Jagadish Rangrej, Andrew D Paterson, et al.
BMC Proceedings|May 10, 2008
Sex, age and generation effects on genome-wide linkage analysis of gene expression in transformed lymphoblastsJagadish Rangrej, Joseph Beyene, Pingzhao Hu, et al.
Diabetes|January 6, 2025
Integrative Proteogenomic Analyses Provide Novel Interpretations of Type 1 Diabetes Risk Loci Through Circulating ProteinsTianyuan Lu, Despoina Manousaki, Lei Sun, et al.
Statistics in Biosciences|March 18, 2024
Statistical Learning of Large-Scale Genetic Data: How to Run a Genome-Wide Association Study of Gene-Expression Data Using the 1000 Genomes Project DataAnton Sugolov, Eric Emmenegger, Andrew D Paterson, et al.
Pageof 28

Showing results (21-30 of 276) with videos related to

Sort By:
Pageof 28
BMC Genetics|February 3, 2006
Evaluating outlier loci and their effect on the identification of pedigree errorsKe-Sheng Wang, Michelle Liu, Andrew D Paterson
Biological Psychiatry|July 18, 2008
Genome-wide linkage analyses of quantitative and categorical autism subphenotypesXiao-Qing Liu, Andrew D Paterson, Peter Szatmari, et al.
BMC Proceedings|December 17, 2016
Factors associated with heterogeneity in microarray gene expression in peripheral blood mononuclear cells from large pedigreesMichael Gallaugher, Angelo J Canty, Andrew D Paterson
Plos One|July 7, 2015
Assessment and Selection of Competing Models for Zero-Inflated Microbiome DataLizhen Xu, Andrew D Paterson, Williams Turpin, et al.
Canadian Journal of Diabetes|March 9, 2026
Insulin response to glucose and type 2 diabetes and its impact on cardiometabolic disease: a sex-stratified Mendelian randomization study to assess potential causalityYuchao Wu, Habiba Hashemy, Andrew D Paterson, et al.
BMC Proceedings|December 19, 2014
Genetic Analysis Workshop 18 single-nucleotide variant prioritization based on protein impact, sequence conservation, and gene annotationThomas Nalpathamkalam, Andriy Derkach, Andrew D Paterson, et al.
BMC Proceedings|May 10, 2008
The multiplicity problem in linkage analysis of gene expression data - the power of differentiating cis- and trans-acting regulatorsBaisong Huang, Jagadish Rangrej, Andrew D Paterson, et al.
BMC Proceedings|May 10, 2008
Sex, age and generation effects on genome-wide linkage analysis of gene expression in transformed lymphoblastsJagadish Rangrej, Joseph Beyene, Pingzhao Hu, et al.
Diabetes|January 6, 2025
Integrative Proteogenomic Analyses Provide Novel Interpretations of Type 1 Diabetes Risk Loci Through Circulating ProteinsTianyuan Lu, Despoina Manousaki, Lei Sun, et al.
Statistics in Biosciences|March 18, 2024
Statistical Learning of Large-Scale Genetic Data: How to Run a Genome-Wide Association Study of Gene-Expression Data Using the 1000 Genomes Project DataAnton Sugolov, Eric Emmenegger, Andrew D Paterson, et al.
Pageof 28