论文标题
预测父母在植物育种中的产量表现：一种神经协作过滤方法
Predicting Yield Performance of Parents in Plant Breeding: A Neural Collaborative Filtering Approach
论文作者
论文摘要
实验性的玉米杂种是在植物育种计划中创建的，通过跨越了两个所谓的近交和测试仪的父母。识别最佳父母组合的交叉组合是具有挑战性的，因为父母可能的交叉组合总数很大，并且由于时间和预算资源有限，测试所有可能的交叉组合是不切实际的。在2020年同步作物挑战赛中，同步释放了几个大型数据集，这些数据集记录了593个近交交叉组合的历史表现约为4％的496个测试仪，这些测试仪在2016年至2018年之间的280个地点种植，并要求参与者预测基于近头和测试者的跨测试人员的跨越群体和测试人员的群体群体和其他群落数据的收益。在本文中，我们提出了一种协作过滤方法，该方法是矩阵分解方法和神经网络的集合来解决此问题。我们的计算结果表明，所提出的模型显着优于其他模型，例如Lasso，Random Forest（RF）和神经网络。提出的方法和结果是在2020年的先正达农作物挑战中产生的。
Experimental corn hybrids are created in plant breeding programs by crossing two parents, so-called inbred and tester, together. Identification of best parent combinations for crossing is challenging since the total number of possible cross combinations of parents is large and it is impractical to test all possible cross combinations due to limited resources of time and budget. In the 2020 Syngenta Crop Challenge, Syngenta released several large datasets that recorded the historical yield performances of around 4% of total cross combinations of 593 inbreds with 496 testers which were planted in 280 locations between 2016 and 2018 and asked participants to predict the yield performance of cross combinations of inbreds and testers that have not been planted based on the historical yield data collected from crossing other inbreds and testers. In this paper, we present a collaborative filtering method which is an ensemble of matrix factorization method and neural networks to solve this problem. Our computational results suggested that the proposed model significantly outperformed other models such as LASSO, random forest (RF), and neural networks. Presented method and results were produced within the 2020 Syngenta Crop Challenge.
