Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650050, China; Computer Technology Application Key Lab of Yunnan Province, Kunming University of Science and Technology, Kunming 650050, China.
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650050, China.
Methods. 2021 Aug;192:13-24. doi: 10.1016/j.ymeth.2020.07.013. Epub 2020 Aug 3.
Cancer can arise as a consequence of the accumulation of genomic alterations. Only a small part of driver mutations contributes to cancer development and progression. Hence, the identification of genes and alterations that serve as drivers for cancer development plays a critical role in drug design, cancer diagnoses and treatment. In this study, we propose a novel method to identify potential cancer drivers by using a Representation Learning method on Attributed Graphs (called RLAG). It is a first attempt to use both network structure and node attributes to learn feature representation for the genes in the network. Then it leverages these feature vectors to divide the genes into several subgroups. Finally, potential cancer driver genes are prioritized according to ranking scores that measure both genes' properties and their importance in the subgroups. We apply our method to predict driver genes for lung cancer, breast cancer and prostate cancer. The results show that our method outperforms the other three state-of-the-art methods in terms of Precision, Recall and F1-score values.
癌症可能是由于基因组改变的积累而产生的。只有一小部分驱动突变有助于癌症的发展和进展。因此，鉴定作为癌症发展驱动因素的基因和改变对于药物设计、癌症诊断和治疗至关重要。在这项研究中，我们提出了一种新的方法，通过使用属性图的表示学习方法（称为 RLAG）来识别潜在的癌症驱动基因。这是首次尝试同时使用网络结构和节点属性来学习网络中基因的特征表示。然后，它利用这些特征向量将基因分成几个亚组。最后，根据衡量基因特性及其在亚组中重要性的排名分数，对潜在的癌症驱动基因进行优先级排序。我们将我们的方法应用于预测肺癌、乳腺癌和前列腺癌的驱动基因。结果表明，我们的方法在精度、召回率和 F1 分数方面优于其他三种最先进的方法。
