disperseNN2: a neural network for estimating dispersal distance from georeferenced polymorphism data.Spatial genetic variation is shaped in part by an organism's dispersal ability.  We present a deep learning tool, disperseNN2, for estimating the mean per-generation dispersal distance from georeferenced polymorphism data.  These attributes led disperseNN2 to outperform a state-of-the-art deep learning method that does not use explicit spatial information: the mean relative absolute error was reduced by 33% and 48% using sample sizes of 10 and 100 individuals, respectively.  disperseNN2 is particularly useful for non-model organisms or systems with sparse genomic resources, as it uses unphased, single nucleotide polymorphisms as its input.  The software is open source and available from https://github.com/kr-colab/disperseNN2 , with documentation located at https://dispersenn2.readthedocs.io/en/latest/ .
disperseNN2
disperseNN2: a neural network for estimating dispersal distance from georeferenced polymorphism data.Spatial genetic variation is shaped in part by an organism's dispersal ability.  We present a deep learning tool, disperseNN2, for estimating the mean per-generation dispersal distance from georeferenced polymorphism data.  These attributes led disperseNN2 to outperform a state-of-the-art deep learning method that does not use explicit spatial information: the mean relative absolute error was reduced by 33% and 48% using sample sizes of 10 and 100 individuals, respectively.  disperseNN2 is particularly useful for non-model organisms or systems with sparse genomic resources, as it uses unphased, single nucleotide polymorphisms as its input.  The software is open source and available from https://github.com/kr-colab/disperseNN2 , with documentation located at https://dispersenn2.readthedocs.io/en/latest/ .
disperseNN2
disperseNN2: a neural network for estimating dispersal distance from georeferenced polymorphism data.Spatial genetic variation is shaped in part by an organism's dispersal ability.  We present a deep learning tool, disperseNN2, for estimating the mean per-generation dispersal distance from georeferenced polymorphism data.  These attributes led disperseNN2 to outperform a state-of-the-art deep learning method that does not use explicit spatial information: the mean relative absolute error was reduced by 33% and 48% using sample sizes of 10 and 100 individuals, respectively.  disperseNN2 is particularly useful for non-model organisms or systems with sparse genomic resources, as it uses unphased, single nucleotide polymorphisms as its input.  The software is open source and available from https://github.com/kr-colab/disperseNN2 , with documentation located at https://dispersenn2.readthedocs.io/en/latest/ .
disperseNN2
disperseNN2: a neural network for estimating dispersal distance from georeferenced polymorphism data.Spatial genetic variation is shaped in part by an organism's dispersal ability.  We present a deep learning tool, disperseNN2, for estimating the mean per-generation dispersal distance from georeferenced polymorphism data.  These attributes led disperseNN2 to outperform a state-of-the-art deep learning method that does not use explicit spatial information: the mean relative absolute error was reduced by 33% and 48% using sample sizes of 10 and 100 individuals, respectively.  disperseNN2 is particularly useful for non-model organisms or systems with sparse genomic resources, as it uses unphased, single nucleotide polymorphisms as its input.  The software is open source and available from https://github.com/kr-colab/disperseNN2 , with documentation located at https://dispersenn2.readthedocs.io/en/latest/ .
disperseNN2
The software is open source and available from https://github.com/kr-colab/disperseNN2 , with documentation located at https://dispersenn2.readthedocs.io/en/latest/ .
https://github.com/kr-colab/disperseNN2
The software is open source and available from https://github.com/kr-colab/disperseNN2 , with documentation located at https://dispersenn2.readthedocs.io/en/latest/ .
https://dispersenn2.readthedocs.io/en/latest/