Discriminating cirRNAs from other lncRNAs using a hierarchical extreme learning machine (H-ELM) algorithm with feature selection

• Published in: Molecular genetics and genomics : MGG Vol. 293; no. 1; pp. 137 - 149
• Main Authors: Chen, Lei, Zhang, Yu-Hang, Huang, Guohua, Pan, Xiaoyong, Wang, ShaoPeng, Huang, Tao, Cai, Yu-Dong
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2018; Springer Nature B.V
• Subjects: algorithms; Animal Genetics and Genomics; Biochemistry; Biomedical and Life Sciences; Computer applications; Conserved sequence; Gene regulation; genes; Graph representations; Human Genetics; Learning algorithms; Life Sciences; Microbial Genetics and Genomics; non-coding RNA; Original Article; Plant Genetics and Genomics; proteins; Ribonucleic acid; RNA; Transcription; transcription (genetics); lncRNAs; cirRNAs; Minimum redundancy maximum relevance; Hierarchical extreme learning machine algorithm
• ISSN: 1617-4615; 1617-4623; 1617-4623
• DOI: 10.1007/s00438-017-1372-7

As non-coding RNAs, circular RNAs (cirRNAs) and long non-coding RNAs (lncRNAs) have attracted an increasing amount of attention. They have been confirmed to participate in many biological processes, including playing roles in transcriptional regulation, regulating protein-coding genes, and binding to RNA-associated proteins. Until now, the differences between these two types of non-coding RNAs have not been fully uncovered. It is still quite difficult to detect cirRNAs from other lncRNAs using simple techniques. In this study, we investigated these two types of non-coding RNAs using several computational methods. The purpose was to extract important factors that could distinguish cirRNAs from other lncRNAs and build an effective classification model to distinguish them. First, we collected cirRNAs, lncRNAs and their representations from a previous study, in which each cirRNA or lncRNA was represented by 188 features derived from its graph representation, sequence and conservation properties. Second, these features were analyzed by the minimum redundancy maximum relevance (mRMR) method. The obtained mRMR feature list, incremental feature selection method and hierarchical extreme learning machine algorithm were employed to build an optimal classification model with sensitivity of 0.703, specificity of 0.850, accuracy of 0.789 and a Matthews correlation coefficient of 0.561. Finally, we analyzed the 16 most important features. Of them, the sequences and structures of the RNA molecule were top ranking, implying they can be potential indicators of differences between cirRNAs and other lncRNAs. Meanwhile, other features of evolutionary conversation, sequence consecution were also important.