Evidence map›Paper›PMID 26085220›Full record

ArticleMolecular genetics and genomics : MGG2016

repRNA: a web server for generating various feature vectors of RNA sequences.

Bin Liu, Fule Liu, Longyun Fang, Xiaolong Wang, Kuo-Chen Chou

Abstract read
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In one paragraph

Article in Molecular genetics and genomics : MGG, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers.

0numbers the graph read from it
0cells of the map it votes in
66citing 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

66 citing papers in PubMed.

  1. Machine Learning for RNA-Targeting Drug Design.Journal of chemical information and modeling · 2026
    Review
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  14. Review
  15. Selecting Essential MicroRNAs Using a Novel Voting Method.Molecular therapy. Nucleic acids · 2019
    Article
  16. Review
  17. Article
  18. Article
  19. Article
  20. Article

6 more citing papers are in PubMed but not listed here.

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

5 authors.

Bin LiuSchool of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, 518055, Guangdong, China. bliu@gordonlifescience.org.
Fule LiuSchool of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, 518055, Guangdong, China. liufule12@gmail.com.
Longyun FangSchool of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, 518055, Guangdong, China. dragoncloudest@gmail.com.
Xiaolong WangSchool of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, 518055, Guangdong, China. wangxl@insun.hit.edu.cn.
Kuo-Chen ChouGordon Life Science Institute, Boston, MA, 02478, USA. kcchou@gordonlifescience.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the rapid growth of RNA sequences generated in the postgenomic age, it is highly desired to develop a flexible method that can generate various kinds of vectors to represent these sequences by focusing on their different features. This is because nearly all the existing machine-learning methods, such as SVM (support vector machine) and KNN (k-nearest neighbor), can only handle vectors but not sequences. To meet the increasing demands and speed up the genome analyses, we have developed a new web server, called "representations of RNA sequences" (repRNA). Compared with the existing methods, repRNA is much more comprehensive, flexible and powerful, as reflected by the following facts: (1) it can generate 11 different modes of feature vectors for users to choose according to their investigation purposes; (2) it allows users to select the features from 22 built-in physicochemical properties and even those defined by users' own; (3) the resultant feature vectors and the secondary structures of the corresponding RNA sequences can be visualized. The repRNA web server is freely accessible to the public at http://bioinformatics.hitsz.edu.cn/repRNA/ .

Indexed as

Base SequenceComputational BiologyGenomeInternetRNASequence Analysis, RNASoftwareSupport Vector MachineRNAPhysicochemical propertiesPseAACPseKNCrepDNArepRNASecondary structure of RNAUser-defined properties

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.