Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
School of Mathematics, Statistics, and Computer Science, College of Science, University of Tehran, Tehran, Iran.
PLoS One. 2021 Aug 9;16(8):e0255718. doi: 10.1371/journal.pone.0255718. eCollection 2021.
Regardless of all efforts on community discovery algorithms, it is still an open and challenging subject in network science. Recognizing communities in a multilayer network, where there are several layers (types) of connections, is even more complicated. Here, we concentrated on a specific type of communities called seed-centric local communities in the multilayer environment and developed a novel method based on the information cascade concept, called PLCDM. Our simulations on three datasets (real and artificial) signify that the suggested method outstrips two known earlier seed-centric local methods. Additionally, we compared it with other global multilayer and single-layer methods. Eventually, we applied our method on a biological two-layer network of Colon Adenocarcinoma (COAD), reconstructed from transcriptomic and post-transcriptomic datasets, and assessed the output modules. The functional enrichment consequences infer that the modules of interest hold biomolecules involved in the pathways associated with the carcinogenesis.
尽管社区发现算法已经取得了很大的进展，但它仍然是网络科学中一个开放且具有挑战性的课题。在多层网络中识别社区（其中存在多种类型的连接）则更加复杂。在这里，我们专注于多层环境中一种特定类型的社区，称为基于种子的局部社区，并基于信息级联的概念开发了一种新方法，称为 PLCDM。我们在三个数据集（真实和人工）上的模拟表明，该方法优于两种已知的基于种子的局部方法。此外，我们还将其与其他全局多层和单层方法进行了比较。最后，我们将该方法应用于从转录组和转录后组学数据集重建的生物双层结肠腺癌（COAD）网络中，并评估了输出模块。功能富集结果表明，感兴趣的模块包含与致癌作用相关途径中的生物分子。
