In Communications biology
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.
Fu Yuhua, Xu Jingya, Tang Zhenshuang, Wang Lu, Yin Dong, Fan Yu, Zhang Dongdong, Deng Fei, Zhang Yanping, Zhang Haohao, Wang Haiyan, Xing Wenhui, Yin Lilin, Zhu Shilin, Zhu Mengjin, Yu Mei, Li Xinyun, Liu Xiaolei, Yuan Xiaohui, Zhao Shuhong