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Prediction of plant pre-microRNAs and their microRNAs in genome-scale sequences using structure-sequence features and support vector machine

Overview of attention for article published in BMC Bioinformatics, December 2014
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (62nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (56th percentile)

Mentioned by

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3 tweeters
weibo
1 weibo user
facebook
2 Facebook pages

Citations

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24 Dimensions

Readers on

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56 Mendeley
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Title
Prediction of plant pre-microRNAs and their microRNAs in genome-scale sequences using structure-sequence features and support vector machine
Published in
BMC Bioinformatics, December 2014
DOI 10.1186/s12859-014-0423-x
Pubmed ID
Authors

Jun Meng, Dong Liu, Chao Sun, Yushi Luan

Abstract

BackgroundMicroRNAs (miRNAs) are a family of non-coding RNAs approximately 21 nucleotides in length that play pivotal roles at the post-transcriptional level in animals, plants and viruses. These molecules silence their target genes by degrading transcription or suppressing translation. Studies have shown that miRNAs are involved in biological responses to a variety of biotic and abiotic stresses. Identification of these molecules and their targets can aid the understanding of regulatory processes. Recently, prediction methods based on machine learning have been widely used for miRNA prediction. However, most of these methods were designed for mammalian miRNA prediction, and few are available for predicting miRNAs in the pre-miRNAs of specific plant species. Although the complete Solanum lycopersicum genome has been published, only 77 Solanum lycopersicum miRNAs have been identified, far less than the estimated number. Therefore, it is essential to develop a prediction method based on machine learning to identify new plant miRNAs.ResultsA novel classification model based on a support vector machine (SVM) was trained to identify real and pseudo plant pre-miRNAs together with their miRNAs. An initial set of 152 novel features related to sequential structures was used to train the model. By applying feature selection, we obtained the best subset of 47 features for use with the Back Support Vector Machine-Recursive Feature Elimination (B-SVM-RFE) method for the classification of plant pre-miRNAs. Using this method, 63 features were obtained for plant miRNA classification. We then developed an integrated classification model, miPlantPreMat, which comprises MiPlantPre and MiPlantMat, to identify plant pre-miRNAs and their miRNAs. This model achieved approximately 90% accuracy using plant datasets from nine plant species, including Arabidopsis thaliana, Glycine max, Oryza sativa, Physcomitrella patens, Medicago truncatula, Sorghum bicolor, Arabidopsis lyrata, Zea mays and Solanum lycopersicum. Using miPlantPreMat, 522 Solanum lycopersicum miRNAs were identified in the Solanum lycopersicum genome sequence.ConclusionsWe developed an integrated classification model, miPlantPreMat, based on structure-sequence features and SVM. MiPlantPreMat was used to identify both plant pre-miRNAs and the corresponding mature miRNAs. An improved feature selection method was proposed, resulting in high classification accuracy, sensitivity and specificity.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 2 4%
Norway 1 2%
Colombia 1 2%
Unknown 52 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 20%
Student > Ph. D. Student 10 18%
Student > Master 10 18%
Professor > Associate Professor 6 11%
Professor 6 11%
Other 9 16%
Unknown 4 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 20 36%
Biochemistry, Genetics and Molecular Biology 12 21%
Computer Science 11 20%
Psychology 3 5%
Physics and Astronomy 1 2%
Other 3 5%
Unknown 6 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 January 2015.
All research outputs
#6,552,947
of 12,378,406 outputs
Outputs from BMC Bioinformatics
#2,383
of 4,542 outputs
Outputs of similar age
#97,866
of 267,251 outputs
Outputs of similar age from BMC Bioinformatics
#67
of 162 outputs
Altmetric has tracked 12,378,406 research outputs across all sources so far. This one is in the 46th percentile – i.e., 46% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,542 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 45th percentile – i.e., 45% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 267,251 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 62% of its contemporaries.
We're also able to compare this research output to 162 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 56% of its contemporaries.