Cingiz, Mustafa ÖzgürDiri, B.2021-03-202021-03-2020199781728128689http://doi.org/10.1109/ASYU48272.2019.8946426https://hdl.handle.net/20.500.12885/12922019 Innovations in Intelligent Systems and Applications Conference, ASYU 2019 -- 31 October 2019 through 2 November 2019 -- -- 156545In recent years, different biological data sets obtained by the next generation sequencing techniques have enhanced the analysis of the underlying molecular interactions of diseases. In our study we apply ARNetMiT, C3NET, WGCNA and ARACNE algorithms on microRNA-Target gene datasets to infer gene coexpression networks of breast, prostate, colon and pancreatic cancers. Gene coexpression networks are evaluated according to their topological and biological features. WGCNA based gene coexpression networks fits to scale free network topology more than other gene coexpression networks. In biological assessment there is no obvious difference found between gene coexpression networks which derived from different algorithms. © 2019 IEEE.trinfo:eu-repo/semantics/closedAccessgene ontology termsmiRNA-Target genesgene network inference algorithmsgene coexpression networksscale free networksConference Object10.1109/ASYU48272.2019.89464262-s2.0-85078345863N/AN/A