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A Computational Framework to Infer Prostate Cancer-Associated Long Noncoding RNAs and Analyses for Identifying a Competing Endogenous RNA Network
Genetic Testing and Molecular Biomarkers  (IF1.795),  Pub Date : 2021-09-17, DOI: 10.1089/gtmb.2021.0053
Roshanak S. Sajjadi, Mohammad Hossein Modarressi, Fahimeh Akbarian, Mohammad Amin Tabatabaiefar

Background: Prostate cancer (PC) is the second leading cause of cancer death after lung cancer in men. Current biomarkers are ineffective for the treatment and management of the disease. Long noncoding RNAs (lncRNAs) are a heterogeneous group of transcripts that are involved in complex gene expression regulatory networks. Although lncRNAs have been suggested to be promising as future biomarkers, the connection between the majority of lncRNAs and human disease remains to be elucidated. One approach to elucidate the roles of lncRNAs in disease is through the development of computational models. For example, a novel computational model termed HyperGeometric distribution for LncRNA-Disease Association (HGLDA) has been developed. Such models need to be developed on a tumor-specific basis to better suit the particular problem.