亚型
组学
水准点(测量)
计算机科学
计算生物学
数据挖掘
机器学习
生物信息学
人工智能
生物
大地测量学
程序设计语言
地理
作者
Ya Li Kuang,Minzhu Xie
标识
DOI:10.1109/bibm58861.2023.10385294
摘要
Identifying cancer subtypes is an essential component of precision medicine, as it helps researchers develop more precise treatment methods and prevention strategies. Meanwhile, high-throughput sequencing technologies have produced a huge amount of multi-omics data for cancer patients and make it is practical to subtype cancers using multi-omics data. As existing cancer subtyping computational models based on multi-omics data could not effectively extended to weakly paired omics data, we proposed a novel unsupervised cancer subtyping model Subtype-DCGCN. Subtype-DCGCN uses Dual Contrast Graph Convolutional Networks guided by dual contrastive learning to lean low dimensional features for each type omics data, and with weighted average fusion Subtype-DCGCN could deal well with weakly paired multi-omics data. Extensive experiments on benchmark datasets showed that Subtype-DCGCN exhibited superior performance to other eight state-of-the-art similar methods in general to identify cancer subtypes. Moreover, tests on simulated datasets with varying missing rate showed that Subtype-DCGCN performed pretty well on weakly paired omics datasets.
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