deepNF: deep network fusion for protein function prediction
deepNF: deep network fusion for protein function prediction
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DOI：
10.1093/bioinformatics/bty440
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发表时间：
2018-11-15
期刊：
影响因子：
5.8
通讯作者：
Bonneau, Richard
中科院分区：
文献类型：
--
作者：
Gligorijevic, Vladimir;Barot, Meet;Bonneau, Richard
动机：高通量实验方法的普及导致了大量的大规模分子和功能相互作用网络。这些网络的连通性为推断基因和蛋白质的功能注释提供了丰富的信息来源。一个重要的挑战是开发方法，结合这些异构网络提取有用的蛋白质功能预测的特征表示。大多数现有的网络集成方法使用浅层模型，难以捕捉复杂和高度非线性的网络结构。因此，我们提出了一种基于Multimodal Deep Autoencoders的网络融合方法deepNF，用于从多个异质相互作用网络中提取蛋白质的高级特征。结果：我们将该方法应用于联合收割机STRING网络，构建了一个包含高级蛋白质特征的通用低维表示。我们在多模态自动编码器的早期阶段为不同的网络类型使用单独的层，然后将所有层连接到一个瓶颈层中，从中提取特征来预测蛋白质功能。我们比较了我们的方法与最先进的方法，包括最近提出的方法Mashup的交叉验证和时间保持预测性能。我们的研究结果表明，我们的方法优于以前的方法为人类和酵母STRING网络。我们还显示了我们的方法在预测不同类型和特异性的基因本体论术语方面的性能的实质性改善。
Motivation: The prevalence of high-throughput experimental methods has resulted in an abundance of large-scale molecular and functional interaction networks. The connectivity of these networks provides a rich source of information for inferring functional annotations for genes and proteins. An important challenge has been to develop methods for combining these heterogeneous networks to extract useful protein feature representations for function prediction. Most of the existing approaches for network integration use shallow models that encounter difficulty in capturing complex and highly non-linear network structures. Thus, we propose deepNF, a network fusion method based on Multimodal Deep Autoencoders to extract high-level features of proteins from multiple heterogeneous interaction networks.Results: We apply this method to combine STRING networks to construct a common low-dimensional representation containing high-level protein features. We use separate layers for different network types in the early stages of the multimodal autoencoder, later connecting all the layers into a single bottleneck layer from which we extract features to predict protein function. We compare the cross-validation and temporal holdout predictive performance of our method with state-of-the-art methods, including the recently proposed method Mashup. Our results show that our method outperforms previous methods for both human and yeast STRING networks. We also show substantial improvement in the performance of our method in predicting gene ontology terms of varying type and specificity.