Evidence map›Paper›PMID 32308930›Full record

ArticleComputational and structural biotechnology journal2020

CirRNAPL: A web server for the identification of circRNA based on extreme learning machine.

Mengting Niu, Jun Zhang, Yanjuan Li, Cankun Wang, Zhaoqian Liu, Hui Ding, Quan Zou, Qin Ma

Abstract read
In one paragraph

Article in Computational and structural biotechnology journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed.

  1. Review
  2. Interpretation of RNA Universe and Coding Potential Using IntRNA.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Mengting NiuInstitute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China.
Jun ZhangRehabilitation Department, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin, China.
Yanjuan LiSchool of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China.
Cankun WangDepartment of Biomedical Informatics, Ohio State University, Columbus, OH, USA.
Zhaoqian LiuDepartment of Biomedical Informatics, Ohio State University, Columbus, OH, USA.
Hui DingCenter for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.
Quan ZouInstitute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China.
Qin MaDepartment of Biomedical Informatics, Ohio State University, Columbus, OH, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Circular RNA (circRNA) plays an important role in the development of diseases, and it provides a novel idea for drug development. Accurate identification of circRNAs is important for a deeper understanding of their functions. In this study, we developed a new classifier, CirRNAPL, which extracts the features of nucleic acid composition and structure of the circRNA sequence and optimizes the extreme learning machine based on the particle swarm optimization algorithm. We compared CirRNAPL with existing methods, including blast, on three datasets and found CirRNAPL significantly improved the identification accuracy for the three datasets, with accuracies of 0.815, 0.802, and 0.782, respectively. Additionally, we performed sequence alignment on 564 sequences of the independent detection set of the third data set and analyzed the expression level of circRNAs. Results showed the expression level of the sequence is positively correlated with the abundance. A user-friendly CirRNAPL web server is freely available at http://server.malab.cn/CirRNAPL/.

Indexed as

ACC, AccuracycircRNA, circular RNACircular RNACNN, Convolutional Neural NetworksDACC, Dinucleotide-based auto-cross-covarianceDAC, Dinucleotide-based auto-covarianceDCC, Dinucleotide-based cross-covarianceELM, extreme learning machineExpression levelExtreme learning machineGAC, Geary autocorrelationIdentificationlncRNAs, long non-coding RNAsMAC, Moran autocorrelationMCC, Matthews Correlation CoefficientMRMD, Maximum-Relevance-Maximum-DistanceNMBAC, Normalized Moreau–Broto autocorrelationParticle swarm optimization algorithmPCGs, protein coding genesPC-PseDNC-General, General parallel correlation pseudo-dinucleotide compositionPseDPC, Pseudo-distance structure status pair compositionPseSSC, Pseudo-structure status compositionPSO, particle swarm optimization algorithmRBF, radial basis functionRF, random forestSC-PseDNC-General, General series correlation pseudo-dinucleotide compositionSE, SensitivitySP, SpecifitySVM, support vector machineTriplet, Local structure-sequence triplet element

Identifiers

PMID32308930
PMCPMC7153170

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.