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Identification of lncRNAs-gene interactions in transcription regulation based on co-expression analysis of RNA-seq data

1 School of Mathematics, Shandong University, Jinan, Shandong 250100, China
2 Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA
3 College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China

Special Issues: Machine Learning in Molecular Biology

Long noncoding RNAs (lncRNA) play important roles in gene expression regulation in diverse biological contexts. Numerous studies have indicated that lncRNA-gene interactions are closely related to the occurrence and development of cancers. Thus, it is important to develop an effective method for the identification of target genes of lncRNA. Meanwhile, the high throughput sequencing data provide tremendous information about regulation correlation, by which the new target genes could be detected from known lncRNA regulated genes. In this study, we developed a method for elucidating lncRNA-gene interactions by using a biclustering approach, which allows for the identification of particular expression patterns across multiple datasets, indicating networks of lncRNA and gene interactions. A p-value strategy is followed to link co-expression patterns to certain lncRNAs. The method was applied on the breast cancer RNA-seq datasets along with a set of known lncRNA regulated genes. The evaluation indicated that the method can detect some new targets but fail to obtain higher coverage. We believe that this developed method will provide useful information for future studies on lncRNAs.
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