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10.1016/j.patter.2020.100057 http://scihub22266oqcxt.onion/10.1016/j.patter.2020.100057 33205126!7660384!33205126     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Patterns+(N+Y) 2020 ; 1 (6): 100057 Nephropedia Template TP {{tp|p=33205126|t=2020. Explaining the Genetic Causality for Complex Phenotype via Deep Association Kernel Learning |pdf=|usr=}}{{33205126}} gab.com Text Explaining the Genetic Causality for Complex Phenotype via Deep Association Kernel Learning JO: Patterns (N Y) http://www.kidney.de/pget.php?&tw=1&pmid=33205126 #FOAvip The genetic effect explains the causality from genetic mutations to the development of complex diseases. Existing genome-wide association study (GWAS) approaches are always built under a linear assumption, restricting their generalization in dissecting complicated causality such as the recessive genetic effect. Therefore, a sophisticated and general GWAS model that can work with different types of genetic effects is highly desired. Here, we introduce a deep association kernel learning (DAK) model to enable automatic causal genotype encoding for GWAS at pathway level. DAK can detect both common and rare variants with complicated genetic effects where existing approaches fail. When applied to four real-world GWAS datasets including cancers and schizophrenia, our DAK discovered potential casual pathways, including the association between dilated cardiomyopathy pathway and schizophrenia. Twit Text FOAVip Explaining the Genetic Causality for Complex Phenotype via Deep Association Kernel Learning ????Patterns (N Y) http://www.kidney.de/pget.php?&tw=1&pmid=33205126 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33205126 #FOAvip English Wikipedia <ref>{{Cite pmid|33205126}}</ref> Explaining the Genetic Causality for Complex Phenotype via Deep Association Kernel Learning #MMPMID33205126 Bao F; Deng Y; Du M; Ren Z; Wan S; Liang KY; Liu S; Wang B; Xin J; Chen F; Christiani DC; Wang M; Dai Q Patterns (N Y) 2020[Sep]; 1 (6): 100057 PMID33205126show ga The genetic effect explains the causality from genetic mutations to the development of complex diseases. Existing genome-wide association study (GWAS) approaches are always built under a linear assumption, restricting their generalization in dissecting complicated causality such as the recessive genetic effect. Therefore, a sophisticated and general GWAS model that can work with different types of genetic effects is highly desired. Here, we introduce a deep association kernel learning (DAK) model to enable automatic causal genotype encoding for GWAS at pathway level. DAK can detect both common and rare variants with complicated genetic effects where existing approaches fail. When applied to four real-world GWAS datasets including cancers and schizophrenia, our DAK discovered potential casual pathways, including the association between dilated cardiomyopathy pathway and schizophrenia. äExplaining the Genetic Causality for Complex Phenotype via Deep Associ(epmc).pdf DeepDyve Pubget Overpricing