Motivation Precise prediction of cancer subtypes is of significant importance in cancer diagnosis and treatment. Disease etiology is complicated existing at different omics levels; hence integrative analysis provides a very effective way to improve our understanding of cancer. Results We propose a novel computational framework, named Deep Subspace Mutual Learning (DSML). DSML has the capability to simultaneously learn the subspace structures in each available omics data and in overall multi-omics data by adopting deep neural networks, which thereby facilitates the subtype’s prediction via clustering on multi-level, single-level and partial-level omics data. Extensive experiments are performed in five different cancers on three levels of omics data from The Cancer Genome Atlas. The experimental analysis demonstrates that DSML delivers comparable or even better results than many state-of-the-art integrative methods. Availability and implementation An implementation and documentation of the DSML is publicly available at https://github.com/polytechnicXTT/Deep-Subspace-Mutual-Learning.git.

中文翻译：

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用于癌症亚型预测的深度子空间相互学习

动机 癌症亚型的精确预测在癌症诊断和治疗中具有重要意义。疾病病因复杂，存在于不同的组学水平；因此，综合分析提供了一种非常有效的方法来提高我们对癌症的理解。结果 我们提出了一种新的计算框架，称为深度子空间相互学习 (DSML)。DSML能够通过采用深度神经网络同时学习每个可用组学数据和整体多组学数据中的子空间结构，从而通过对多级、单级和部分级组学数据的聚类来促进亚型预测. 对来自癌症基因组图谱的三个组学数据在五种不同的癌症中进行了广泛的实验。实验分析表明，与许多最先进的综合方法相比，DSML 提供了可比甚至更好的结果。可用性和实施​​ DSML 的实施和文档可在 https://github.com/polytechnicXTT/Deep-Subspace-Mutual-Learning.git 上公开获得。