Learning representation for multiple biological networks via a robust graph regularized integration approach
Learning representation for multiple biological networks via a robust graph regularized integration approach
复制标题
通过稳健的图正则化集成方法学习多个生物网络的表示
DOI：
10.1093/bib/bbab409
复制
发表时间：
2021-10
影响因子：
9.5
通讯作者：
Dao-Qing Dai
中科院分区：
文献类型：
--
作者：
Xiwen Zhang;Weiwen Wang;Chuan-Xian Ren;Dao-Qing Dai
抽象的。学习节点表示是生物网络分析中的一个基本问题，因为紧凑的表示特征揭示了复杂的网络结构，并为链路预测和节点分类等下游任务提供了有用的信息。最近，从不同方面分析对象的多个网络日益积累，为从多个角度了解对象提供了机会。然而，不同网络中复杂的公共和特定信息对节点表示方法提出了挑战。此外，网络中无处不在的噪声要求更稳健的表示。针对这些问题，我们提出了一种多生物网络的表示学习方法。首先，我们使用去噪扩散来适应网络中的噪声和虚假边缘，为后续的表示学习提供健壮的连通性结构。然后，我们引入了一种图正则化集成模型来结合精化网络和计算公共表示特征。通过使用正则化分解技术，该模型可以有效地保留不同网络的共同结构属性，同时容纳它们的特定信息，从而获得一致的表示。仿真研究表明，该方法在不同级别的噪声网络中具有较好的性能。使用从该方法学习的表示特征，进行了药物-靶点相互作用预测、基因功能识别和细粒度物种分类三个基于网络的推理任务。涉及不同规模和稀疏程度的生物网络。在真实数据上的实验结果表明，与其他方法相比，该方法具有较强的鲁棒性。总体而言，通过消除噪声和有效集成，该方法能够从多个生物网络中学习有用的表示。
Abstract. Learning node representation is a fundamental problem in biological network analysis, as compact representation features reveal complicated network structures and carry useful information for downstream tasks such as link prediction and node classification. Recently, multiple networks that profile objects from different aspects are increasingly accumulated, providing the opportunity to learn objects from multiple perspectives. However, the complex common and specific information across different networks pose challenges to node representation methods. Moreover, ubiquitous noise in networks calls for more robust representation. To deal with these problems, we present a representation learning method for multiple biological networks. First, we accommodate the noise and spurious edges in networks using denoised diffusion, providing robust connectivity structures for the subsequent representation learning. Then, we introduce a graph regularized integration model to combine refined networks and compute common representation features. By using the regularized decomposition technique, the proposed model can effectively preserve the common structural property of different networks and simultaneously accommodate their specific information, leading to a consistent representation. A simulation study shows the superiority of the proposed method on different levels of noisy networks. Three network-based inference tasks, including drug–target interaction prediction, gene function identification and fine-grained species categorization, are conducted using representation features learned from our method. Biological networks at different scales and levels of sparsity are involved. Experimental results on real-world data show that the proposed method has robust performance compared with alternatives. Overall, by eliminating noise and integrating effectively, the proposed method is able to learn useful representations from multiple biological networks.
登录
查看更多内容
利用本体图图来预测稀疏注释的基因功能。
DOI：
10.1093/bioinformatics/btv260
发表时间：
2015-06-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Wang S;Cho H;Zhai C;Berger B;Peng J
通讯作者：
Peng J
通过压缩数据融合和链接的基因优先次序。
影响因子：
4.3
作者：
Žitnik M;Nam EA;Dinh C;Kuspa A;Shaulsky G;Zupan B
通讯作者：
Zupan B
DOI：
10.1093/bioinformatics/btn162
发表时间：
2008-07-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Yamanishi Y;Araki M;Gutteridge A;Honda W;Kanehisa M
通讯作者：
Kanehisa M
影响因子：
4.6
作者：
Zitnik, Marinka;Janjic, Vuk;Larminie, Chris;Zupan, Blaz;Przulj, Natasa
通讯作者：
Przulj, Natasa
影响因子：
2.7
作者：
Zhu, CY;Byrd, RH;Nocedal, J
通讯作者：
Nocedal, J