Oncogene is a special type of genes, which can promote the tumor initiation. Good study on oncogenes is helpful for understanding the cause of cancers. Experimental techniques in early time are quite popular in detecting oncogenes. However, their defects become more and more evident in recent years, such as high cost and long time. The newly proposed computational methods provide an alternative way to study oncogenes, which can provide useful clues for further investigations on candidate genes. Considering the limitations of some previous computational methods, such as lack of learning procedures and terming genes as individual subjects, a novel computational method was proposed in this study. The method adopted the features derived from multiple protein networks, viewing proteins in a system level. A classic machine learning algorithm, random forest, was applied on these features to capture the essential characteristic of oncogenes, thereby building the prediction model. All genes except validated oncogenes were ranked with a measurement yielded by the prediction model. Top genes were quite different from potential oncogenes discovered by previous methods, and they can be confirmed to become novel oncogenes. It was indicated that the newly identified genes can be essential supplements for previous results.

中文翻译：

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网络嵌入方法和随机森林鉴定潜在致癌基因

癌基因是一种特殊的基因，可以促进肿瘤的发生。对癌基因进行良好的研究有助于理解癌症的原因。早期的实验技术在检测癌基因中非常流行。然而，近年来，它们的缺陷变得越来越明显，例如高成本和长时间。新提出的计算方法为研究癌基因提供了另一种方法，可以为进一步研究候选基因提供有用的线索。考虑到一些以前的计算方法的局限性，例如缺乏学习程序和将基因称为单个主题，本研究提出了一种新颖的计算方法。该方法采用了源自多个蛋白质网络的功能，可以在系统级别查看蛋白质。经典的机器学习算法，将随机森林应用于这些特征以捕获癌基因的基本特征，从而建立预测模型。除验证的致癌基因外，所有基因均按预测模型得出的测量结果进行排名。顶级基因与以前方法发现的潜在致癌基因完全不同，可以确定它们是新的致癌基因。结果表明，新鉴定的基因可以作为先前结果的必要补充。