HiSCF: leveraging higher-order structures for clustering analysis in biological networks
HiSCF: leveraging higher-order structures for clustering analysis in biological networks
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DOI：
10.1093/bioinformatics/btaa775
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发表时间：
2021-02-15
期刊：
影响因子：
5.8
通讯作者：
You, Zhu-Hong
中科院分区：
文献类型：
--
作者：
Hu, Lun;Zhang, Jun;You, Zhu-Hong
动机：生物网络中的聚类分析是将生物实体分组为功能模块，从而为理解复杂的生物系统提供有价值的见解。现有的聚类技术利用低阶的连接模式在一个单独的生物实体和它们的连接的水平，但很少有人可以考虑到高阶的连接模式在一个小的网络motifs.Results的水平：在这里，我们提出了一种新的聚类框架，即HiSCF，识别功能模块的高阶结构信息的基础上，在生物网络。利用高阶马尔可夫随机过程，HiSCF能够通过利用各种网络图案来执行聚类分析。当与几个国家的最先进的聚类模型相比，HiSCF产生最好的性能为两个实际的聚类应用，即蛋白质复合物识别和基因共表达模块检测，在准确性方面。HiSCF的良好性能表明，高阶网络基序的考虑为生物网络的分析提供了新的见解，例如识别重叠蛋白质复合物和推断新的信号通路，并且还揭示了生物网络中存在的丰富的高阶组织结构。
Motivation: Clustering analysis in a biological network is to group biological entities into functional modules, thus providing valuable insight into the understanding of complex biological systems. Existing clustering techniques make use of lower-order connectivity patterns at the level of individual biological entities and their connections, but few of them can take into account of higher-order connectivity patterns at the level of small network motifs.Results: Here, we present a novel clustering framework, namely HiSCF, to identify functional modules based on the higher-order structure information available in a biological network. Taking advantage of higher-order Markov stochastic process, HiSCF is able to perform the clustering analysis by exploiting a variety of network motifs. When compared with several state-of-the-art clustering models, HiSCF yields the best performance for two practical clustering applications, i.e. protein complex identification and gene co-expression module detection, in terms of accuracy. The promising performance of HiSCF demonstrates that the consideration of higher-order network motifs gains new insight into the analysis of biological networks, such as the identification of overlapping protein complexes and the inference of new signaling pathways, and also reveals the rich higher-order organizational structures presented in biological networks.