Deep polygenic neural network for predicting and identifying yield-associated genes in Indonesian rice accessions.

ABSTRACT: As the fourth most populous country in the world, Indonesia must increase the annual rice production rate to achieve national food security by 2050. One possible solution comes from the nanoscopic level: a genetic variant called Single Nucleotide Polymorphism (SNP), which can express significant yield-associated genes. The prior benchmark of this study utilized a statistical genetics model where no SNP position information and attention mechanism were involved. Hence, we developed a novel deep polygenic neural network, named the NucleoNet model, to address these obstacles. The NucleoNets were constructed with the combination of prominent components that include positional SNP encoding, the context vector, wide models, Elastic Net, and Shannon's entropy loss. This polygenic modeling obtaine

SUBMITTER: Dominic N 

PROVIDER: S-EPMC9378700 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature