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10.7554/eLife.54507 http://scihub22266oqcxt.onion/10.7554/eLife.54507 32511092!7324158!32511092     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Elife 2020 ; 9 (ä): ä Nephropedia Template TP {{tp|p=32511092|t=2020. Predicting geographic location from genetic variation with deep neural networks |pdf=|usr=}}{{32511092}} gab.com Text Predicting geographic location from genetic variation with deep neural networks JO: Elife http://www.kidney.de/pget.php?&tw=1&pmid=32511092 #FOAvip Most organisms are more closely related to nearby than distant members of their species, creating spatial autocorrelations in genetic data. This allows us to predict the location of origin of a genetic sample by comparing it to a set of samples of known geographic origin. Here, we describe a deep learning method, which we call Locator, to accomplish this task faster and more accurately than existing approaches. In simulations, Locator infers sample location to within 4.1 generations of dispersal and runs at least an order of magnitude faster than a recent model-based approach. We leverage Locator's computational efficiency to predict locations separately in windows across the genome, which allows us to both quantify uncertainty and describe the mosaic ancestry and patterns of geographic mixing that characterize many populations. Applied to whole-genome sequence data from Plasmodium parasites, Anopheles mosquitoes, and global human populations, this approach yields median test errors of 16.9km, 5.7km, and 85km, respectively. Twit Text FOAVip Predicting geographic location from genetic variation with deep neural networks ????Elife http://www.kidney.de/pget.php?&tw=1&pmid=32511092 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32511092 #FOAvip English Wikipedia <ref>{{Cite pmid|32511092}}</ref> Predicting geographic location from genetic variation with deep neural networks #MMPMID32511092 Battey CJ; Ralph PL; Kern AD Elife 2020[Jun]; 9 (ä): ä PMID32511092show ga Most organisms are more closely related to nearby than distant members of their species, creating spatial autocorrelations in genetic data. This allows us to predict the location of origin of a genetic sample by comparing it to a set of samples of known geographic origin. Here, we describe a deep learning method, which we call Locator, to accomplish this task faster and more accurately than existing approaches. In simulations, Locator infers sample location to within 4.1 generations of dispersal and runs at least an order of magnitude faster than a recent model-based approach. We leverage Locator's computational efficiency to predict locations separately in windows across the genome, which allows us to both quantify uncertainty and describe the mosaic ancestry and patterns of geographic mixing that characterize many populations. Applied to whole-genome sequence data from Plasmodium parasites, Anopheles mosquitoes, and global human populations, this approach yields median test errors of 16.9km, 5.7km, and 85km, respectively. |*Genetic Variation[MESH] |*Neural Networks, Computer[MESH] |Animal Distribution[MESH] |Animals[MESH] |Anopheles/*genetics/parasitology[MESH] |Demography[MESH] |Genome, Human[MESH] |Genomics/*methods[MESH] |Genotype[MESH] |Humans[MESH]Predicting geographic location from genetic variation with deep neural(epmc).pdf DeepDyve Pubget Overpricing