Genomic Selection (GS) is a breeding technique that utilizes whole genome markers to make trait predictions. The goal of GS is to identify the top candidates that have the most desirable trait values. Usually, GS has been formulated as a regression problem where the marker data is used to predict phenotypic values. However, since the end goal of GS is identification of top candidates, ranking the individuals makes far more sense. Creating accurate ranking models pose three fundamental challenges—presence of noise in phenotypic data, extremely high dimensional nature of the genotypic data and small sample size of the genomic datasets. To combat these challenges, we present a novel two phase approach to increase the noise tolerance of ranking based approaches. The proposed algorithm uses pruning to perform noise filtering and leverages biclustering to improve model generalization. This approach is evaluated on both pointwise and pairwise ranking algorithms. Previous work on Arabidopsis and CIMMYT wheat datasets yielded mean Normalized Discounted Cumulative Gain (NDCG) @10 scores of 0.883 and 0.748 respectively. The proposed approach outperforms these results on both of the datasets yielding ranking accuracies of 0.965 and 0.865 respectively.

基因组选择（GS）是一种育种技术，利用整个基因组标记进行性状预测。GS的目标是确定具有最理想特征值的顶级候选者。通常，GS已被公式化为回归问题，其中标记数据用于预测表型值。但是，由于GS的最终目标是确定最佳人选，因此对个人进行排名就更有意义了。创建准确的排名模型会带来三个基本挑战：表型数据中是否存在噪声，基因型数据的极高维本质和基因组数据集的样本量小。为了应对这些挑战，我们提出了一种新颖的两阶段方法，以提高基于等级的方法的噪声容忍度。所提出的算法使用修剪来执行噪声过滤，并利用二次聚类来提高模型的泛化能力。这种方法在点对和成对排名算法中都得到了评估。以前的工作拟南芥和CIMMYT小麦数据集的10均值归一化贴现累积增益（NDCG）分别为0.883和0.748。所提出的方法在两个数据集上均优于这些结果，分别产生0.965和0.865的排名精度。