Background: As potent gene regulators, long noncoding RNAs (lncRNAs) are critical in
various biological activities, such as cellular processes. With the development of new sequencing
technologies, vast amount of transcriptome data are available, which require efficient computational
tools to distinguish noncoding RNAs from their coding counterparts, especially for lncRNAs.
Methods: In this paper, we review the advancement of computational methods in predicting lncRNAs,
summarize the difficulties in developing machine learning algorithms, and point out a few promising future
directions. We also briefly summarize and describe popular softwares and web-servers in the area.
Results and Conclusion: Given the exponentially expanding transcriptome data and increasing
importance of lncRNAs in disease development and treatment, novel and effective computational tools
for identifying lncRNAs are highly demanded.