An Association Rule Mining Approach to Discover lncRNAs Expression Patterns in Cancer Datasets.

ABSTRACT: In the past few years, the role of long noncoding RNAs (lncRNAs) in tumor development and progression has been disclosed although their mechanisms of action remain to be elucidated. An important contribution to the comprehension of lncRNAs biology in cancer could be obtained through the integrated analysis of multiple expression datasets. However, the growing availability of public datasets requires new data mining techniques to integrate and describe relationship among data. In this perspective, we explored the powerness of the Association Rule Mining (ARM) approach in gene expression data analysis. By the ARM method, we performed a meta-analysis of cancer-related microarray data which allowed us to identify and characterize a set of ten lncRNAs simultaneously altered in different brain t

SUBMITTER: Cremaschi P 

PROVIDER: S-EPMC4530207 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature