A propagation-based seed-centric local community detection for multilayer environment: The case study of colon adenocarcinoma

• Published in: PloS one Vol. 16; no. 8; p. e0255718
• Main Authors: Pournoor, Ehsan, Mousavian, Zaynab, Nowzari-Dalini, Abbas, Masoudi-Nejad, Ali
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 09.08.2021; Public Library of Science (PLoS)
• Subjects: Adenocarcinoma; Adenocarcinoma - genetics; Adenocarcinoma - metabolism; Algorithms; Analysis; Binding sites; Biochemistry; Bioinformatics; Biology; Biology and Life Sciences; Biomolecules; Biophysics; Cancer; Carcinogenesis; Carcinogenesis - genetics; Carcinogens; Care and treatment; Case reports; Colon; Colon cancer; Colonic Neoplasms - genetics; Colonic Neoplasms - metabolism; Colorectal cancer; Community; Computer science; Datasets; Diagnosis; Ecology and Environmental Sciences; Friendship; Humans; Laboratories; Local communities; Medicine and Health Sciences; Methods; Modules; Monolayers; Multilayers; Physical Sciences; Propagation; Protein Interaction Maps - genetics; Research and Analysis Methods; Seeds; Simulation; Social networks; Transcriptome - genetics; Iran
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0255718

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.