Deep learning technologies are permeating every field from image and speech recognition to computational and systems biology. However, the application of convolutional neural networks (CCNs) to “omics” data poses some difficulties, such as the processing of complex networks structures as well as its integration with transcriptome data. Here, we propose a CNN approach that combines spectral clustering information processing to classify lung cancer. The developed spectral-convolutional neural network based method achieves success in integrating protein interaction network data and gene expression profiles to classify lung cancer. The performed computational experiments suggest that in terms of accuracy the predictive performance of our proposed method was better than those of other machine learning methods such as SVM or Random Forest. Moreover, the computational results also indicate that the underlying protein network structure assists to enhance the predictions. Data and CNN code can be downloaded from the link: https://sites.google.com/site/nacherlab/analysis

中文翻译：

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整合蛋白质相互作用网络和基因表达谱的肺癌分类卷积神经网络方法

深度学习技术正在渗透到从图像和语音识别到计算和系统生物学的各个领域。然而，卷积神经网络（CCNs）在“组学”数据中的应用带来了一些困难，例如复杂网络结构的处理以及与转录组数据的整合。在这里，我们提出了一种结合光谱聚类信息处理对肺癌进行分类的 CNN 方法。开发的基于光谱卷积神经网络的方法在整合蛋白质相互作用网络数据和基因表达谱以对肺癌进行分类方面取得了成功。进行的计算实验表明，就准确性而言，我们提出的方法的预测性能优于其他机器学习方法，如 SVM 或随机森林。而且，计算结果还表明，潜在的蛋白质网络结构有助于增强预测。数据和CNN代码可以从链接下载：https://sites.google.com/site/nacherlab/analysis