The Comparative Study of Algorithms in Building the Green Mobile Cloud Computing Environment

Dominic, Nicholas; Prayoga, Jonathan Sebastian; Kumala, Daniel; Surantha, Nico; Soewito, Benfano

doi:10.1007/978-3-030-89899-1_5

Cited by 2 publications

(1 citation statement)

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“…After the genotype dosage imputation by the Bayesian Imputation Based Association Mapping (BIMBAM) software for SNPs with call rate beyond 25% and removal of monomorphic SNP, 697 rice samples × 1232 SNPs were obtained. The alternative imputation services are Online Plant-ImputeDB or Rice Imputation Server 44 which utilized cloud computational offloading technology 45 . Note that before the imputation, referring to the raw data we received, the call rate of 9 significant SNPs is 0.222% for TBGI036687, 1.774% for TBGI050092, 0.665% for id4009920, 1.109% for id5014338, 1.330% for both TBGI272457 and id8000244, 20.843% for id7002427, 2.217% for id10003620, and 0% for id12006560.…”

Section: Methodsmentioning

confidence: 99%

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

Dominic

Cenggoro

Budiarto

et al. 2022

Sci Rep

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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 obtained up to 2.779 of Mean Squared Error (MSE) with 47.156% of Symmetric Mean Absolute Percentage Error (SMAPE), while revealing 15 new important SNPs. Furthermore, the NucleoNets reduced the MSE score up to 32.28% compared to the Ordinary Least Squares (OLS) model. Through the ablation study, we learned that the combination of Xavier distribution for weights initialization and Normal distribution for biases initialization sparked more various important SNPs throughout 12 chromosomes. Our findings confirmed that the NucleoNet model was successfully outperformed the OLS model and identified important SNPs to Indonesian rice yields.

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Section: Methodsmentioning

confidence: 99%