College of Biomedical Engineering, Taiyuan University of Technology, Jinzhong, Shanxi, 030600, People's Republic of China.
College of Computer Science & Technology, Taiyuan University of Technology, Jinzhong, Shanxi, 030600, People's Republic of China.
Future Oncol. 2023 Jun;19(20):1429-1438. doi: 10.2217/fon-2023-0070. Epub 2023 Jul 25.
To develop a deep learning-based multiomics integration model. Five types of omics data (mRNA, DNA methylation, miRNA, copy number variation and protein expression) were used to build a deep learning-based multiomics integration model a deep neural network, incorporating an attention mechanism that adaptively considers the weights of multiomics features. Compared with other methods, the deep learning-based multiomics integration model achieved remarkable results, with an area under the curve of 0.89 (95% CI: 0.863-0.910). The deep learning-based multiomics integration model achieved promising results and is an effective method for predicting axillary lymph node metastasis in breast cancer.
为了开发一种基于深度学习的多组学整合模型。 使用五种类型的组学数据（mRNA、DNA 甲基化、miRNA、拷贝数变异和蛋白质表达）构建了一种基于深度学习的多组学整合模型，该模型采用了一种注意力机制的深度神经网络，可以自适应地考虑多组学特征的权重。 与其他方法相比，基于深度学习的多组学整合模型取得了显著的效果，曲线下面积为 0.89（95%CI：0.863-0.910）。 基于深度学习的多组学整合模型取得了有前景的结果，是预测乳腺癌腋窝淋巴结转移的有效方法。
