Convolutional neural network approach to lung cancer classification integrating protein interaction network and gene expression profiles
Convolutional neural network approach to lung cancer classification integrating protein interaction network and gene expression profiles
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DOI：
10.1142/s0219720019400079
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发表时间：
2019-06-01
影响因子：
1
通讯作者：
Nacher, Jose C.
中科院分区：
文献类型：
--
作者：
Matsubara, Teppei;Ochiai, Tomoshiro;Nacher, Jose C.
深度学习技术正渗透到从图像和语音识别到计算生物学和系统生物学的各个领域。然而，卷积神经网络（CNNs）在“组学”数据中的应用存在一些困难，比如复杂网络结构的处理以及它与转录组数据的整合。在此，我们提出一种结合谱聚类信息处理的卷积神经网络方法来对肺癌进行分类。所开发的基于谱 - 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。所进行的计算实验表明，就准确性而言，我们所提出方法的预测性能优于其他机器学习方法，如支持向量机（SVM）或随机森林。此外，计算结果还表明潜在的蛋白质网络结构有助于提高预测效果。
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 pro files 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.