Abstract Rice plays a pivotal role as a vital food source for human consumption. Identifying gene‐phenotype associations (GPAs) can significantly enhance the tolerance of rice to environmental stress and its overall yield. Nevertheless, the experimental process of discovering GPAs is not only consume a lot of resources but also time‐consuming. The computational screening for GPAs has emerged as an essential tool to complement and expedite biological experiments. In this study, we tackle the prediction of GPAs by framing it as a node classification task, and introduce RGPA‐GCN, an innovative computational approach leveraging graph convolutional networks. RGPA‐GCN constructs a topology graph through the application of the k‐nearest neighbor method for effective information aggregation. The nodes within this graph encapsulate both gene functional similarity and phenotype semantic similarity, enhancing the accuracy of our predictions. Notably, the RGPA‐GCN approach demonstrates its ability to predict both unknown GPAs and previously unseen genes or phenotypes. Leveraging 5‐fold cross‐validation, RGPA‐GCN exhibits commendable performance, outperforming six classical machine learning methods, and three state‐of‐the‐art models. Additionally, the ablation studies on the sampler and the case studies involving five different phenotypes yields promising results, underscoring the effectiveness of this approach.
Luo et al. (Sun,) studied this question.