GLIDE: combining local methods and diffusion state embeddings to predict missing interactions in biological networks
GLIDE: combining local methods and diffusion state embeddings to predict missing interactions in biological networks
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DOI：
10.1093/bioinformatics/btaa459
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发表时间：
2020-07-01
期刊：
影响因子：
5.8
通讯作者：
Cowen, Lenore J.
中科院分区：
文献类型：
--
作者：
Devkota, Kapil;Murphy, James M.;Cowen, Lenore J.
动机：生物网络分析的核心问题之一是链路预测问题。特别是，现有的交互网络是真实网络的嘈杂且不完整的快照，缺少许多真实的链接，因为这些交互尚未通过实验观察到。与生物网络相比，预测缺失链接的方法在社交网络中得到了更广泛的研究。最近有人认为，蛋白质-蛋白质相互作用（PPI）网络数据中存在一些特殊结构，这可能意味着替代方法可能优于社交网络的最佳方法。基于扩散状态距离的推广，我们设计了一种新的基于嵌入的链接预测方法，称为全局和局部集成扩散嵌入（GLIDE）。 GLIDE 旨在有效捕获全局网络结构，并结合捕获本地网络结构的替代网络类型特定的定制措施。我们在严格的交叉验证实验中对源自 2016 年 DREAM 疾病模块识别挑战的三个最近策划的人类生物网络以及酵母 PPI 网络的经典版本的集合进行了 GLIDE 测试。结果：我们确实发现不同类型的生物网络中不同的局部网络结构占主导地位。我们发现简单的本地网络测量在中心基因之间的高度连接的网络核心中占主导地位，但 GLIDE 的全局嵌入测量在网络的其余部分增加了价值。例如，我们从已知与克罗恩病有关的基因到未知的关联基因进行基于 GLIDE 的链接预测，并做出一些新的预测，在其他网络数据和文献中寻找支持。
Motivation: One of the core problems in the analysis of biological networks is the link prediction problem. In particular, existing interactions networks are noisy and incomplete snapshots of the true network, with many true links missing because those interactions have not yet been experimentally observed. Methods to predict missing links have been more extensively studied for social than for biological networks; it was recently argued that there is some special structure in protein-protein interaction (PPI) network data that might mean that alternate methods may outperform the best methods for social networks. Based on a generalization of the diffusion state distance, we design a new embedding-based link prediction method called global and local integrated diffusion embedding (GLIDE). GLIDE is designed to effectively capture global network structure, combined with alternative network type-specific customized measures that capture local network structure. We test GLIDE on a collection of three recently curated human biological networks derived from the 2016 DREAM disease module identification challenge as well as a classical version of the yeast PPI network in rigorous cross validation experiments.Results: We indeed find that different local network structure is dominant in different types of biological networks. We find that the simple local network measures are dominant in the highly connected network core between hub genes, but that GLIDE's global embedding measure adds value in the rest of the network. For example, we make GLIDE-based link predictions from genes known to be involved in Crohn's disease, to genes that are not known to have an association, and make some new predictions, finding support in other network data and the literature.