将基因型数据转为张量输入残差网络，显著提升作物性状预测准确率。
ResGene-T: A Tensor-Based Residual Network Approach for Genomic Prediction
- 用三维张量表示基因型，替代传统二维图像输入
- 在三个作物十个性状上，相比主流模型提升14.51%至41.51%
- 适合从事基因组预测与深度学习交叉研究的学者
本文提出一种新型深度学习模型ResGene-T，用于基因组预测（GP），即关联基因型与表型。传统方法将基因型作为字符序列输入一维卷积网络，而受早期将基因型表示为二维图像启发，本文构建了基于ResNet-18的ResGene-2D模型。尽管二维表示能捕捉生物互作，但需全层参与，训练效率低，仅带来3%性能提升。为此，本文创新性地将二维图像转换为三维张量输入残差网络，形成新模型ResGene-T。在三种作物、十个性状的数据集上，与七种主流模型（两个统计、两个机器学习、三个深度学习）对比，ResGene-T表现最优，性能提升幅度达14.51%至41.51%。
原文摘要 · Abstract (English)
In this work, we propose a new deep learning model for Genomic Prediction (GP), which involves correlating genotypic data with phenotypic. The genotypes are typically fed as a sequence of characters to the 1D-Convolution Neural Network layer of the underlying deep learning model. Inspired by earlier work that represented genotype as a 2D-image for genotype-phenotype classification, we extend this idea to GP, which is a regression task. We use a ResNet-18 as the underlying architecture, and term this model as ResGene-2D. Although the 2D-image representation captures biological interactions well, it requires all the layers of the model to do so. This limits training efficiency. Thus, as seen in the earlier work that proposed a 2D-image representation, our ResGene-2D performs almost the same as other models (3% improvement). To overcome this, we propose a novel idea of converting the 2D-image into a 3D/ tensor and feed this to the ResNet-18 architecture, and term this model as ResGene-T. We evaluate our proposed models on three crop species having ten phenotypic traits and compare it with seven most popular models (two statistical, two machine learning, and three deep learning). ResGene-T performs the best among all these seven methods (gains from 14.51% to 41.51%).
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