Skip to main content
SpringerLink
Log in

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

  • Original Article
  • Published:
Molecular Genetics and Genomics Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Allard G, Ryan FJ, Jeffery IB, Claesson MJ (2015) SPINGO: a rapid species-classifier for microbial amplicon sequences. BMC Bioinform 16:324

    Article  CAS  Google Scholar 

  • Barbosa C, Peixeiro I, Romao L (2013) Gene expression regulation by upstream open reading frames and human disease. PLoS Genet 9:e1003529

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Barreau C, Paillard L, Osborne HB (2005) AU-rich elements and associated factors: are there unifying principles? Nucleic Acids Res 33:7138–7150

    Article  CAS  PubMed  Google Scholar 

  • Beck A, Teboulle M (2009) A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J Imaging Sci 2:183–202

    Article  Google Scholar 

  • Breiman L (2001) Random forests. Mach Learn 45:5–32

    Article  Google Scholar 

  • Cai H, Xue Y, Wang P, Wang Z, Li Z, Hu Y, Li Z, Shang X, Liu Y (2015a) The long noncoding RNA TUG1 regulates blood-tumor barrier permeability by targeting miR-144. Oncotarget 6:19759–19779

    PubMed  PubMed Central  Google Scholar 

  • Cai Z, Xu D, Zhang Q, Zhang J, Ngai S-M, Shao J (2015b) Classification of lung cancer using ensemble-based feature selection and machine learning methods. Mol BioSyst 11:791–800

    Article  CAS  PubMed  Google Scholar 

  • Cao J, Xiong L (2014) Protein sequence classification with improved extreme learning machine algorithms. Biomed Res Int 2014:103054

    PubMed  PubMed Central  Google Scholar 

  • Carnemolla B, Balza E, Siri A, Zardi L, Nicotra MR, Bigotti A, Natali PG (1989) A tumor-associated fibronectin isoform generated by alternative splicing of messenger RNA precursors. J Cell Biol 108:1139–1148

    Article  CAS  PubMed  Google Scholar 

  • Chen LL, Yang L (2015) Regulation of circRNA biogenesis. RNA Biol 12:381–388

    Article  PubMed  PubMed Central  Google Scholar 

  • Chen L, Feng KY, Cai YD, Chou KC, Li HP (2010) Predicting the network of substrate-enzyme-product triads by combining compound similarity and functional domain composition. BMC Bioinform 11:293

    Article  CAS  Google Scholar 

  • Chen I, Chen CY, Chuang TJ (2015a) Biogenesis, identification, and function of exonic circular RNAs. Wiley Interdiscip Rev RNA 6:563–579

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Chen L, Chu C, Huang T, Kong X, Cai YD (2015b) Prediction and analysis of cell-penetrating peptides using pseudo-amino acid composition and random forest models. Amino Acids 47:1485–1493

    Article  CAS  PubMed  Google Scholar 

  • Chen L, Chu C, Feng K (2016a) Predicting the types of metabolic pathway of compounds using molecular fragments and sequential minimal optimizatiom. Comb Chem High Throughput Screen 19:136–143

    Article  CAS  PubMed  Google Scholar 

  • Chen L, Chu C, Zhang Y-H, Zhu C, Kong X, Huang T, Cai Y-D (2016b) Analysis of gene expression profiles in the brain stem, cerebellum and cerebral cortex. PLoS One 11:e0159395

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Chen L, Zhang Y-H, Huang T, Cai Y-D (2016c) Gene expression profiling gut microbiota in different races of humans. Sci Rep 6:23075

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Chen L, Zhang Y-H, Zheng M, Huang T, Cai Y-D (2016d) Identification of compound-protein interactions through the analysis of gene ontology, KEGG enrichment for proteins and molecular fragments of compounds. Mol Genet Genom 291:2065–2079

    Article  CAS  Google Scholar 

  • Chen L, Zhang Y-H, Lu G, Huang T, Cai Y-D (2017) Analysis of cancer-related lncRNAs using gene ontology and KEGG pathways. Artif Intell Med 76:27–36

    Article  PubMed  Google Scholar 

  • Colombrita C, Onesto E, Tiloca C, Ticozzi N, Silani V, Ratti A (2011) RNA-binding proteins and RNA metabolism: a new scenario in the pathogenesis of amyotrophic lateral sclerosis. Arch Ital Biol 149:83–99

    PubMed  Google Scholar 

  • Conn SJ, Pillman KA, Toubia J, Conn VM, Salmanidis M, Phillips CA, Roslan S, Schreiber AW, Gregory PA, Goodall GJ (2015) The RNA binding protein quaking regulates formation of circRNAs. Cell 160:1125–1134

    Article  CAS  PubMed  Google Scholar 

  • Corinna Cortes VV (1995) Support-vector networks. Mach Learn 20:273–297

    Google Scholar 

  • Daliri MR (2015) Combining extreme learning machines using support vector machines for breast tissue classification. Comput Methods Biomech Biomed Eng 18:185–191

    Article  Google Scholar 

  • Draminski M, Rada-Iglesias A, Enroth S, Wadelius C, Koronacki J, Komorowski J (2008) Monte Carlo feature selection for supervised classification. Bioinformatics 24:110–117

    Article  CAS  PubMed  Google Scholar 

  • Dramiński M, Kierczak M, Nowak-Brzezińska A, Koronecki J, Komorowski J (2011) The Monte Carlo feature selection and interdependency discovery is unbiased. Control Cybern 40:199–211

    Google Scholar 

  • Ebbesen KK, Kjems J, Hansen TB (2016) Circular RNAs: identification, biogenesis and function. Biochim Biophys Acta 1859:163–168

    Article  CAS  PubMed  Google Scholar 

  • Evtushenko EV, Levitsky VG, Elisafenko EA, Gunbin KV, Belousov AI, Safar J, Dolezel J, Vershinin AV (2016) The expansion of heterochromatin blocks in rye reflects the co-amplification of tandem repeats and adjacent transposable elements. BMC Genom 17:337

    Article  CAS  Google Scholar 

  • Fang Y, Chen L (2017) A binary classifier for prediction of the types of metabolic pathway of chemicals. Comb Chem High Throughput Screen 20:140–146

    Article  CAS  PubMed  Google Scholar 

  • Folkman L, Yang Y, Li Z, Stantic B, Sattar A, Mort M, Cooper DN, Liu Y, Zhou Y (2015) DDIG-in: detecting disease-causing genetic variations due to frameshifting indels and nonsense mutations employing sequence and structural properties at nucleotide and protein levels. Bioinform 31:1599–1606

    Article  CAS  Google Scholar 

  • Freedman JE, Miano JM, Inst NHLB (2017) Challenges and opportunities in linking long noncoding RNAs to cardiovascular, lung, and blood diseases. Arterioscler Thromb Vasc Biol 37:21–25

    Article  CAS  PubMed  Google Scholar 

  • Gao S, Tian H, Guo Y, Li Y, Guo Z, Zhu X, Chen X (2015) miRNA oligonucleotide and sponge for miRNA-21 inhibition mediated by PEI-PLL in breast cancer therapy. Acta Biomater 25:184–193

    Article  CAS  PubMed  Google Scholar 

  • Geisler S, Coller J (2013) RNA in unexpected places: long non-coding RNA functions in diverse cellular contexts. Nat Rev Mol Cell Biol 14:699–712

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Ghosal S, Das S, Chakrabarti J (2013) Long noncoding RNAs: new players in the molecular mechanism for maintenance and differentiation of pluripotent stem cells. Stem Cells Dev 22:2240–2253

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Glazar P, Papavasileiou P, Rajewsky N (2014) circBase: a database for circular RNAs. RNA 20:1666–1670

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Gloss BS, Dinger ME (2016) The specificity of long noncoding RNA expression. Biochim Biophys Acta 1859:16–22

    Article  CAS  PubMed  Google Scholar 

  • Gong J, Liu W, Zhang JY, Miao XP, Guo AY (2015) IncRNASNP: a database of SNPs in lncRNAs and their potential functions in human and mouse. Nucleic Acids Res 43:D181–D186

    Article  CAS  PubMed  Google Scholar 

  • Guil S, Esteller M (2015) RNA–RNA interactions in gene regulation: the coding and noncoding players. Trends Biochem Sci 40:248–256

    Article  CAS  PubMed  Google Scholar 

  • Guo JU, Agarwal V, Guo HL, Bartel DP (2014) Expanded identification and characterization of mammalian circular RNAs. Genome Biol 15:409

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Hadjiargyrou M, Delihas N (2013) The intertwining of transposable elements and non-coding RNAs. Int J Mol Sci 14:13307–13328

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Hansen TB, Jensen TI, Clausen BH, Bramsen JB, Finsen B, Damgaard CK, Kjems J (2013) Natural RNA circles function as efficient microRNA sponges. Nature 495:384–388

    Article  CAS  PubMed  Google Scholar 

  • Hansen TB, Veno MT, Damgaard CK, Kjems J (2016) Comparison of circular RNA prediction tools. Nucleic Acids Res 44:e58

    Article  PubMed  Google Scholar 

  • Harrow J, Frankish A, Gonzalez JM, Tapanari E, Diekhans M, Kokocinski F, Aken BL, Barrell D, Zadissa A, Searle S, Barnes I, Bignell A, Boychenko V, Hunt T, Kay M, Mukherjee G, Rajan J, Despacio-Reyes G, Saunders G, Steward C, Harte R, Lin M, Howald C, Tanzer A, Derrien T, Chrast J, Walters N, Balasubramanian S, Pei B, Tress M, Rodriguez JM, Ezkurdia I, van Baren J, Brent M, Haussler D, Kellis M, Valencia A, Reymond A, Gerstein M, Guigo R, Hubbard TJ (2012) GENCODE: the reference human genome annotation for the ENCODE project. Genome Res 22:1760–1774

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Holley CL, Topkara VK (2011) An introduction to small non-coding RNAs: miRNA and snoRNA. Cardiovasc Drugs Ther 25:151–159

    Article  CAS  PubMed  Google Scholar 

  • Huang G-B (2014) An insight into extreme learning machines: random neurons, random features and kernels. Cogn Comput 6:376–390

    Article  Google Scholar 

  • Huang GB, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern B Cybern 42:513–529

    Article  PubMed  Google Scholar 

  • Huang T, Wang M, Cai YD (2015) Analysis of the preferences for splice codes across tissues. Protein Cell 6:904–907

    Article  PubMed  PubMed Central  Google Scholar 

  • Ibrahim W, Abadeh MS (2017) Extracting features from protein sequences to improve deep extreme learning machine for protein fold recognition. J Theor Biol 421:1–15

    Article  CAS  PubMed  Google Scholar 

  • Jeck WR, Sorrentino JA, Wang K, Slevin MK, Burd CE, Liu J, Marzluff WF, Sharpless NE (2013) Circular RNAs are abundant, conserved, and associated with ALU repeats. RNA 19:141–157

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Jiang Y, Malouf GG, Zhang J, Zheng X, Chen Y, Thompson EJ, Weinstein JN, Yuan Y, Spano JP, Broaddus R, Tannir NM, Khayat D, Lu KH, Su X (2015) Long non-coding RNA profiling links subgroup classification of endometrioid endometrial carcinomas with trithorax and polycomb complex aberrations. Oncotarget 6:39865–39876

    PubMed  PubMed Central  Google Scholar 

  • Jin GF, Sun JL, Isaacs SD, Wiley KE, Kim ST, Chu LW, Zhang Z, Zhao H, Zheng SL, Isaacs WB, Xu JF (2011) Human polymorphisms at long non-coding RNAs (lncRNAs) and association with prostate cancer risk. Carcinogenesis 32:1655–1659

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Johnson R, Guigo R (2014) The RIDL hypothesis: transposable elements as functional domains of long noncoding RNAs. RNA 20:959–976

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Jung J, Yeom C, Choi YS, Kim S, Lee E, Park MJ, Kang SW, Kim SB, Chang S (2015) Simultaneous inhibition of multiple oncogenic miRNAs by a multi-potent microRNA sponge. Oncotarget 6:20370–20387

    PubMed  PubMed Central  Google Scholar 

  • Kashi K, Henderson L, Bonetti A, Carninci P (2016) Discovery and functional analysis of lncRNAs: methodologies to investigate an uncharacterized transcriptome. Biochim Biophys Acta 1859:3–15

    Article  CAS  PubMed  Google Scholar 

  • Kelley D, Rinn J (2012) Transposable elements reveal a stem cell-specific class of long noncoding RNAs. Genome Biol 13:R107

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Khalil AM, Guttman M, Huarte M, Garber M, Raj A, Morales DR, Thomas K, Presser A, Bernstein BE, van Oudenaarden A, Regev A, Lander ES, Rinn JL (2009) Many human large intergenic noncoding RNAs associate with chromatin-modifying complexes and affect gene expression. Proc Natl Acad Sci USA 106:11667–11672

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: International joint conference on artificial intelligence. Lawrence Erlbaum, Mahwah, pp 1137–1145

  • Kononenko I, Simec E, RobnikSikonja M (1997) Overcoming the myopia of inductive learning algorithms with RELIEFF. Appl Intell 7:39–55

    Article  Google Scholar 

  • Kriegs JO, Churakov G, Jurka J, Brosius J, Schmitz J (2007) Evolutionary history of 7SL RNA-derived SINEs in supraprimates. Trends Genet 23:158–161

    Article  CAS  PubMed  Google Scholar 

  • Kwak SH, Lee SH (2001) The regulation of ornithine decarboxylase gene expression by sucrose and small upstream open reading frame in tomato (Lycopersicon esculentum Mill). Plant Cell Physiol 42:314–323

    Article  CAS  PubMed  Google Scholar 

  • Levin HL, Moran JV (2011) Dynamic interactions between transposable elements and their hosts. Nat Rev Genet 12:615–627

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Li Z, Zhou X, Dai Z, Zou X (2010) Classification of G-protein coupled receptors based on support vector machine with maximum relevance minimum redundancy and genetic algorithm. BMC Bioinform 11:325

    Article  CAS  Google Scholar 

  • Li F, Li C, Wang M, Webb GI, Zhang Y, Whisstock JC, Song J (2015) GlycoMine: a machine learning-based approach for predicting N-, C- and O-linked glycosylation in the human proteome. Bioinformatics 31:1411–1419

    Article  CAS  PubMed  Google Scholar 

  • Li BQ, Zheng LL, Hu LL, Feng KY, Huang G, Chen L (2016a) Prediction of linear B-cell epitopes with mRMR feature selection and analysis. Curr Bioinform 11:22–31

    Article  CAS  Google Scholar 

  • Li TW, Mo XY, Fu LY, Xiao BX, Guo JM (2016b) Molecular mechanisms of long noncoding RNAs on gastric cancer. Oncotarget 7:8601–8612

    PubMed  PubMed Central  Google Scholar 

  • Liang DM, Wilusz JE (2014) Short intronic repeat sequences facilitate circular RNA production. Gene Dev 28:2233–2247

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Lin C, Chen W, Qiu C, Wu Y, Krishnan S, Zou Q (2014) LibD3C: ensemble classifiers with a clustering and dynamic selection strategy. Neurocomputing 123:424–435

    Article  Google Scholar 

  • Liu L, Chen L, Zhang Y-H, Wei L, Cheng S, Kong X-Y, Zheng M, Huang T, Cai Y-D (2017) Analysis and prediction of drug-drug interaction by minimum redundancy maximum relevance and incremental feature selection. J Biomol Struct Dyn 35:312–329

    Article  CAS  PubMed  Google Scholar 

  • Luk ACS, Chan WY, Rennert OM, Lee TL (2014) Long noncoding RNAs in spermatogenesis: insights from recent high-throughput transcriptome studies. Reproduction 147:R131–R141

    Article  CAS  PubMed  Google Scholar 

  • Ma X, Guo J, Sun X (2015) Sequence-based prediction of RNA-binding proteins using random forest with minimum redundancy maximum relevance feature selection. Biomed Res Int 2015:425810

    PubMed  PubMed Central  Google Scholar 

  • Martini A, Janssen H, Bilhou-Nabera C, La Starza R, Corveleyn A, Mecucci C, Hagemeijer A, Marynen P (2002) The TET RNA-binding proteins, EWSR1 and TAF15, are involved in acute lymphoblastic leukemia, through fusion with a new transcription factor, CIZ/NMP4. Blood 100:528a–528a

    Google Scholar 

  • Maticzka D, Lange SJ, Costa F, Backofen R (2014) GraphProt: modeling binding preferences of RNA-binding proteins. Genome Biol 15:R17

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Matthews BW (1975) Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim Biophys Acta 405:442–451

    Article  CAS  PubMed  Google Scholar 

  • Meijlink F, Curran T, Miller AD, Verma IM (1985) Removal of a 67-base-pair sequence in the noncoding Region of protooncogene-fos converts it to a transforming gene. Proc Natl Acad Sci USA 82:4987–4991

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Meyer D, Leisch F, Hornik K (2003) The support vector machine under test. Neurocomputing 55:169–186

    Article  Google Scholar 

  • Mirza AH, Kaur S, Brorsson CA, Pociot F (2014) Effects of GWAS-associated genetic variants on lncRNAs within IBD and T1D candidate loci. PLoS One 9:e105723

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Ni Q, Chen L (2017) A feature and algorithm selection method for improving the prediction of protein structural classes. Comb Chem High Throughput Screen. doi:10.2174/1386207320666170314103147

    PubMed  Google Scholar 

  • Nitsche A, Rose D, Fasold M, Reiche K, Stadler PF (2015) Comparison of splice sites reveals that long noncoding RNAs are evolutionarily well conserved. RNA 21:801–812

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Pan XY, Xiong K (2015) PredcircRNA: computational classification of circular RNA from other long non-coding RNA using hybrid features. Mol BioSyst 11:2219–2226

    Article  CAS  PubMed  Google Scholar 

  • Peng H, Long F, Ding C (2005) Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE T Pattern Anal 27:1226–1238

    Article  Google Scholar 

  • Peng L, Yuan XQ, Li GC (2015) The emerging landscape of circular RNA ciRS-7 in cancer (review). Oncol Rep 33:2669–2674

    Article  CAS  PubMed  Google Scholar 

  • Piskol R, Ramaswami G, Li JB (2013) Reliable identification of genomic variants from RNA-seq data. Am J Hum Genet 93:641–651

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Qu S, Yang X, Li X, Wang J, Gao Y, Shang R, Sun W, Dou K, Li H (2015) Circular RNA: a new star of noncoding RNAs. Cancer Lett 365:141–148

    Article  CAS  PubMed  Google Scholar 

  • Ryvkin P, Leung YY, Ungar LH, Gregory BD, Wang LS (2014) Using machine learning and high-throughput RNA sequencing to classify the precursors of small non-coding RNAs. Methods 67:28–35

    Article  CAS  PubMed  Google Scholar 

  • Shung CY, Sunter G (2009) Regulation of Tomato golden mosaic virus AL2 and AL3 gene expression by a conserved upstream open reading frame. Virology 383:310–318

    Article  CAS  PubMed  Google Scholar 

  • Sieliwanowicz B (1988) Splicing of nuclear messenger RNA precursors. Postepy Biochem 34:351–360

    CAS  PubMed  Google Scholar 

  • Skipper KA, Andersen PR, Sharma N, Mikkelsen JG (2013) DNA transposon-based gene vehicles—scenes from an evolutionary drive. J Biomed Sci 20:92

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Steffen P, Voss B, Rehmsmeier M, Reeder J, Giegerich R (2006) RNAshapes: an integrated RNA analysis package based on abstract shapes. Bioinformatics 22:500–503

    Article  CAS  PubMed  Google Scholar 

  • Suay L, Salvador ML, Abesha E, Klein U (2005) Specific roles of 5′ RNA secondary structures in stabilizing transcripts in chloroplasts. Nucleic Acids Res 33:4754–4761

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Sun K, Chen X, Jiang P, Song X, Wang H, Sun H (2013) iSeeRNA: identification of long intergenic non-coding RNA transcripts from transcriptome sequencing data. BMC Genom 14(Suppl 2):S7

    Article  Google Scholar 

  • Takahashi K, Yan I, Haga H, Patel T (2014) Long noncoding RNA in liver diseases. Hepatology 60:744–753

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Tang J, Deng C, Huang GB (2016) Extreme learning machine for multilayer perceptron. IEEE Trans Neural Netw Learn Syst 27:809–821

    Article  PubMed  Google Scholar 

  • Tani H, Imamachi N, Mizutani R, Imamura K, Kwon Y, Miyazaki S, Maekawa S, Suzuki Y, Akimitsu N (2015) Genome-wide analysis of long noncoding RNA turnover. Methods Mol Biol 1262:305–320

    Article  CAS  PubMed  Google Scholar 

  • Thomas LF, Saetrom P (2014) Circular RNAs are depleted of polymorphisms at microRNA binding sites. Bioinformatics 30:2243–2246

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Ulitsky I, Shkumatava A, Jan CH, Sive H, Bartel DP (2011) Conserved Function of lincRNAs in vertebrate embryonic development despite rapid sequence evolution. Cell 147:1537–1550

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Ullu E, Tschudi C (1984) Alu sequences are processed 7sl RNA genes. Nature 312:171–172

    Article  CAS  PubMed  Google Scholar 

  • Wang S, Zhang YH, Lu J, Cui W, Hu J, Cai YD (2016) Analysis and identification of aptamer–compound interactions with a maximum relevance minimum redundancy and nearest neighbor algorithm. Biomed Res Int 2016:8351204

    PubMed  PubMed Central  Google Scholar 

  • Wang S, Zhang Y-H, Huang G, Chen L, Cai Y-D (2017) Analysis and prediction of myristoylation sites using the mRMR Method, the IFS method and an extreme learning machine algorithm. Comb Chem High Throughput Screen 20:96–106

    CAS  PubMed  Google Scholar 

  • Weiss K, Antoniou A, Schratt G (2015) Non-coding mechanisms of local mRNA translation in neuronal dendrites. Eur J Cell Biol 94:363–367

    Article  CAS  PubMed  Google Scholar 

  • Worthington MT, Pelo JW, Sachedina MA, Applegate JL, Arseneau KO, Pizarro TT (2002) RNA binding properties of the AU-rich element-binding recombinant Nup475/TIS11/tristetraprolin protein. J Biol Chem 277:48558–48564

    Article  CAS  PubMed  Google Scholar 

  • Xu Y (2011) Chemistry in human telomere biology: structure, function and targeting of telomere DNA/RNA. Chem Soc Rev 40:2719–2740

    Article  CAS  PubMed  Google Scholar 

  • Xu Y, Komiyama M (2012) Structure, function and targeting of human telomere RNA. Methods 57:100–105

    Article  CAS  PubMed  Google Scholar 

  • Yan K, Arfat Y, Li DJ, Zhao F, Chen ZH, Yin C, Sun YL, Hu LF, Yang TM, Qian AR (2016) Structure prediction: new insights into decrypting long noncoding RNAs. Int J Mol Sci 17:E132

    Article  PubMed  CAS  Google Scholar 

  • Yang JX, Rastetter RH, Wilhelm D (2016) Non-coding RNAs: an Introduction. Adv Exp Med Biol 886:13–32

    Article  CAS  PubMed  Google Scholar 

  • Yoon JH, Kim J, Gorospe M (2015) Long noncoding RNA turnover. Biochimie 117:15–21

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • You Z-H, Lei Y-K, Zhu L, Xia J, Wang B (2013) Prediction of protein–protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis. BMC Bioinform 14:S10

    Article  Google Scholar 

  • You Z-H, Li S, Gao X, Luo X, Ji Z (2014) Large-scale protein–protein interactions detection by integrating big biosensing data with computational model. Biomed Res Int 2014:598129

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Zeng X, Lin W, Guo M, Zou Q (2017) A comprehensive overview and evaluation of circular RNA detection tools. PLoS Comput Biol 13:e1005420

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Zhang Y, Ding C, Li T (2008) Gene selection algorithm by combining reliefF and mRMR. BMC Genom 9:S27

    Article  Google Scholar 

  • Zhang K, Shi ZM, Chang YN, Hu ZM, Qi HX, Hong W (2014) The ways of action of long non-coding RNAs in cytoplasm and nucleus. Gene 547:1–9

    Article  CAS  PubMed  Google Scholar 

  • Zhang PW, Chen L, Huang T, Zhang N, Kong XY, Cai YD (2015) Classifying ten types of major cancers based on reverse phase protein array profiles. PLoS One 10:e0123147

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Zou Q, Zeng J, Cao L, Ji R (2016) A novel features ranking metric with application to scalable visual and bioinformatics data classification. Neurocomputing 173:346–354

    Article  Google Scholar 

Download references

Author information

Author notes

    Authors and Affiliations

    1. College of Life Science, Shanghai University, Shanghai, 200444, People’s Republic of China

      Lei Chen, ShaoPeng Wang & Yu-Dong Cai

    2. College of Information Engineering, Shanghai Maritime University, Shanghai, 201306, People’s Republic of China

      Lei Chen

    3. Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, People’s Republic of China

      Yu-Hang Zhang & Tao Huang

    4. Department of Mathematics, Shaoyang University, Shaoyang, 422000, Hunan, People’s Republic of China

      Guohua Huang

    5. Department of Medical Informatics, Erasmus MC, Rotterdam, The Netherlands

      Xiaoyong Pan

    Authors
    1. Lei Chen
      View author publications

      You can also search for this author in PubMed Google Scholar

    2. Yu-Hang Zhang
      View author publications

      You can also search for this author in PubMed Google Scholar

    3. Guohua Huang
      View author publications

      You can also search for this author in PubMed Google Scholar

    4. Xiaoyong Pan
      View author publications

      You can also search for this author in PubMed Google Scholar

    5. ShaoPeng Wang
      View author publications

      You can also search for this author in PubMed Google Scholar

    6. Tao Huang
      View author publications

      You can also search for this author in PubMed Google Scholar

    7. Yu-Dong Cai
      View author publications

      You can also search for this author in PubMed Google Scholar

    Corresponding authors

    Correspondence to Tao Huang or Yu-Dong Cai.

    Ethics declarations

    Funding

    This study was supported by the National Natural Science Foundation of China (31371335, 61672356, 31701151), Natural Science Foundation of Shanghai (17ZR1412500), Shanghai Sailing Program, The Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS) (2016245), Hunan Natural Science Foundation (2017JJ2239) and Scientific Research Fund of Hunan Provincial Education Department (15B216).

    Conflict of interest

    All authors declare that they have no conflict of interest.

    Ethical approval

    This article does not contain any studies with human participants performed by any of the authors.

    Additional information

    Communicated by S. Hohmann.

    Lei Chen and Yu-Hang Zhang contributed equally to this work.

    Electronic supplementary material

    Rights and permissions

    Reprints and permissions

    About this article

    Check for updates. Verify currency and authenticity via CrossMark

    Cite this article

    Chen, L., Zhang, YH., Huang, G. et al. Discriminating cirRNAs from other lncRNAs using a hierarchical extreme learning machine (H-ELM) algorithm with feature selection. Mol Genet Genomics 293, 137–149 (2018). https://doi.org/10.1007/s00438-017-1372-7

    Download citation

    • Received:

    • Accepted:

    • Published:

    • Issue Date:

    • DOI: https://doi.org/10.1007/s00438-017-1372-7

    Keywords

    Search

    Navigation