Data mining of the cancer-related lncRNAs GO terms and KEGG pathways by using mRMR method

摘要

LncRNAs plays an important role in the regulation of gene expression. Identification of cancer-related lncRNAs GO terms and KEGG pathways is great helpful for revealing cancer-related functional biological processes. Therefore, in this study, we proposed a computational method to identify novel cancer-related IncRNAs GO terms and KEGG pathways. By using existing lncRNA database and Max-relevance MM-redundancy (mRMR) method, GO terms and KEGG pathways were evaluated based on their importance on distinguishing cancerrelated and non-cancer-related lncRNAs. Finally, GO terms and KEGG pathways with high importance were presented and analyzed. Our literature reviewing showed that the top 10 ranked GO terms and pathways were really related to interpretable tumorigenesis according to recent publications.