School of Mathematics and Big Data, Foshan University, Foshan, 528000, China.
School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, 510000, China.
BMC Bioinformatics. 2024 Feb 29;25(1):88. doi: 10.1186/s12859-024-05716-7.
Predicting outcome of breast cancer is important for selecting appropriate treatments and prolonging the survival periods of patients. Recently, different deep learning-based methods have been carefully designed for cancer outcome prediction. However, the application of these methods is still challenged by interpretability. In this study, we proposed a novel multitask deep neural network called UISNet to predict the outcome of breast cancer. The UISNet is able to interpret the importance of features for the prediction model via an uncertainty-based integrated gradients algorithm. UISNet improved the prediction by introducing prior biological pathway knowledge and utilizing patient heterogeneity information.
The model was tested in seven public datasets of breast cancer, and showed better performance (average C-index = 0.691) than the state-of-the-art methods (average C-index = 0.650, ranged from 0.619 to 0.677). Importantly, the UISNet identified 20 genes as associated with breast cancer, among which 11 have been proven to be associated with breast cancer by previous studies, and others are novel findings of this study.
Our proposed method is accurate and robust in predicting breast cancer outcomes, and it is an effective way to identify breast cancer-associated genes. The method codes are available at: https://github.com/chh171/UISNet .
预测乳腺癌的预后对于选择合适的治疗方法和延长患者的生存周期非常重要。最近，不同的基于深度学习的方法已经被精心设计用于癌症预后预测。然而，这些方法的应用仍然受到可解释性的挑战。在本研究中，我们提出了一种名为 UISNet 的新型多任务深度神经网络，用于预测乳腺癌的预后。UISNet 能够通过基于不确定性的集成梯度算法为预测模型解释特征的重要性。UISNet 通过引入先验生物学途径知识和利用患者异质性信息来提高预测性能。
该模型在七个公共乳腺癌数据集上进行了测试，表现优于最先进的方法（平均 C 指数= 0.691）（平均 C 指数= 0.650，范围从 0.619 到 0.677）。重要的是，UISNet 确定了 20 个与乳腺癌相关的基因，其中 11 个已被先前的研究证明与乳腺癌相关，其他则是本研究的新发现。
我们提出的方法在预测乳腺癌预后方面准确且稳健，是识别乳腺癌相关基因的有效方法。该方法的代码可在：https://github.com/chh171/UISNet 获得。
