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 signif...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-08-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-16075-9 |
| _version_ | 1811283804005859328 |
|---|---|
| author | Nicholas Dominic Tjeng Wawan Cenggoro Arif Budiarto Bens Pardamean |
| author_facet | Nicholas Dominic Tjeng Wawan Cenggoro Arif Budiarto Bens Pardamean |
| author_sort | Nicholas Dominic |
| collection | DOAJ |
| description | 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 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. |
| first_indexed | 2024-04-13T02:18:09Z |
| format | Article |
| id | doaj.art-74aec2005e394d31bc3f88f22147aee6 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-13T02:18:09Z |
| publishDate | 2022-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-74aec2005e394d31bc3f88f22147aee62022-12-22T03:07:04ZengNature PortfolioScientific Reports2045-23222022-08-0112111610.1038/s41598-022-16075-9Deep polygenic neural network for predicting and identifying yield-associated genes in Indonesian rice accessionsNicholas Dominic0Tjeng Wawan Cenggoro1Arif Budiarto2Bens Pardamean3BINUS Graduate Program, Bina Nusantara UniversitySchool of Computer Science, Bina Nusantara UniversitySchool of Computer Science, Bina Nusantara UniversityBINUS Graduate Program, Bina Nusantara UniversityAbstract 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.https://doi.org/10.1038/s41598-022-16075-9 |
| spellingShingle | Nicholas Dominic Tjeng Wawan Cenggoro Arif Budiarto Bens Pardamean Deep polygenic neural network for predicting and identifying yield-associated genes in Indonesian rice accessions Scientific Reports |
| title | Deep polygenic neural network for predicting and identifying yield-associated genes in Indonesian rice accessions |
| title_full | Deep polygenic neural network for predicting and identifying yield-associated genes in Indonesian rice accessions |
| title_fullStr | Deep polygenic neural network for predicting and identifying yield-associated genes in Indonesian rice accessions |
| title_full_unstemmed | Deep polygenic neural network for predicting and identifying yield-associated genes in Indonesian rice accessions |
| title_short | Deep polygenic neural network for predicting and identifying yield-associated genes in Indonesian rice accessions |
| title_sort | deep polygenic neural network for predicting and identifying yield associated genes in indonesian rice accessions |
| url | https://doi.org/10.1038/s41598-022-16075-9 |
| work_keys_str_mv | AT nicholasdominic deeppolygenicneuralnetworkforpredictingandidentifyingyieldassociatedgenesinindonesianriceaccessions AT tjengwawancenggoro deeppolygenicneuralnetworkforpredictingandidentifyingyieldassociatedgenesinindonesianriceaccessions AT arifbudiarto deeppolygenicneuralnetworkforpredictingandidentifyingyieldassociatedgenesinindonesianriceaccessions AT benspardamean deeppolygenicneuralnetworkforpredictingandidentifyingyieldassociatedgenesinindonesianriceaccessions |