Recent Progress in Long Noncoding RNAs Prediction

Title:Recent Progress in Long Noncoding RNAs Prediction

Volume: 13 Issue: 4

Author(s): Yuhua Yao*, Xianhong Li, Lili Geng, Xuying Nan, Zhaohui Qi and Bo Liao*

Affiliation:

• School of Mathematics and Statistics, Hainan Normal University, Haikou 571158,China

• School of Mathematics and Statistics, Hainan Normal University, Haikou 571158,China

Keywords: Long noncoding RNA, support vector machine, Random Forest, CPC, LncRScan-SVM.

Abstract: 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.

Cite this article as:

Yao Yuhua*, Li Xianhong , Geng Lili , Nan Xuying , Qi Zhaohui and Liao Bo *, Recent Progress in Long Noncoding RNAs Prediction, Current Bioinformatics 2018; 13 (4) . https://dx.doi.org/10.2174/1574893612666170905153933