IEEE/ACM Trans Comput Biol Bioinform. 2023 Nov-Dec;20(6):3499-3510. doi: 10.1109/TCBB.2023.3293472. Epub 2023 Dec 25.
Due to the complexity of cancer pathogenesis at different omics levels, it is necessary to find a comprehensive method to accurately distinguish and find cancer subtypes for cancer treatment. In this paper, we proposed a new cancer multi-omics subtype identification method, which is based on variational autoencoder measured by Wasserstein distance and graph autoencoder (WVGMO). This method depends on two foremost models. The first model is a variational autoencoder measured by Wasserstein distance (WVAE), which is used to extract potential spatial information of each omic data type. The second model is the graph autoencoder (GAE) with the second-order proximity. It has the capability to retain the topological structure information and feature information of the multi-omics data. And then, the identification of cancer subtypes via k-means clustering. Extensive experiments were conducted on seven different cancers based on four omics data from TCGA. The results show that WVGMO provides equivalent or even better results than the most of advanced synthesis methods.
由于癌症在不同组学层面上的发病机制复杂，因此需要找到一种全面的方法来准确区分和发现癌症亚型，以进行癌症治疗。在本文中，我们提出了一种新的癌症多组学亚型识别方法，该方法基于 Wasserstein 距离和图自动编码器（WVGMO）测量的变分自动编码器。该方法依赖于两个主要模型。第一个模型是基于 Wasserstein 距离（WVAE）测量的变分自动编码器，用于提取每个组学数据类型的潜在空间信息。第二个模型是具有二阶接近度的图自动编码器（GAE），它具有保留多组学数据的拓扑结构信息和特征信息的能力。然后，通过 k-均值聚类进行癌症亚型的识别。在基于 TCGA 的四个组学数据的七种不同癌症上进行了广泛的实验。结果表明，WVGMO 提供的结果与最先进的综合方法相当，甚至更好。
