The analyses of multi-omics data have revealed candidate genes for objective traits. However, they are integrated poorly, especially in non-model organisms, and they pose a great challenge for prioritizing candidate genes for follow-up experimental verification. Here, we present a general convolutional neural network model that integrates multi-omics information to prioritize the candidate genes of objective traits. By applying this model to Sus scrofa, which is a non-model organism, but one of the most important livestock animals, the model precision was 72.9%, recall 73.5%, and F1-Measure 73.4%, demonstrating a good prediction performance compared with previous studies in Arabidopsis thaliana and Oryza sativa. Additionally, to facilitate the use of the model, we present ISwine (http://iswine.iomics.pro/), which is an online comprehensive knowledgebase in which we incorporated almost all the published swine multi-omics data. Overall, the results suggest that the deep learning strategy will greatly facilitate analyses of multi-omics integration in the future.

多组学数据的分析揭示了客观性状的候选基因。然而，它们的整合性较差，尤其是在非模式生物中，它们对优先考虑候选基因进行后续实验验证提出了巨大挑战。在这里，我们提出了一个通用的卷积神经网络模型，该模型集成了多组学信息来优先考虑客观性状的候选基因。将该模型应用于非模式生物，但却是最重要的家畜之一的野猪，模型精度为 72.9%，召回率为 73.5%，F1-Measure 为 73.4%，表现出良好的预测性能。以前对拟南芥和水稻的研究. 此外，为了方便模型的使用，我们提供了 ISwine (http://iswine.iomics.pro/)，这是一个在线综合知识库，我们将几乎所有已发表的猪多组学数据纳入其中。总体而言，结果表明深度学习策略将极大地促进未来多组学整合的分析。