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Automatic learning of pre-miRNAs from different species

Overview of attention for article published in BMC Bioinformatics, May 2016
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Title
Automatic learning of pre-miRNAs from different species
Published in
BMC Bioinformatics, May 2016
DOI 10.1186/s12859-016-1036-3
Pubmed ID
Authors

Ivani de O. N. Lopes, Alexander Schliep, André P. de L. F. de Carvalho

Abstract

Discovery of microRNAs (miRNAs) relies on predictive models for characteristic features from miRNA precursors (pre-miRNAs). The short length of miRNA genes and the lack of pronounced sequence features complicate this task. To accommodate the peculiarities of plant and animal miRNAs systems, tools for both systems have evolved differently. However, these tools are biased towards the species for which they were primarily developed and, consequently, their predictive performance on data sets from other species of the same kingdom might be lower. While these biases are intrinsic to the species, their characterization can lead to computational approaches capable of diminishing their negative effect on the accuracy of pre-miRNAs predictive models. We investigate in this study how 45 predictive models induced for data sets from 45 species, distributed in eight subphyla/classes, perform when applied to a species different from the species used in its induction. Our computational experiments show that the separability of pre-miRNAs and pseudo pre-miRNAs instances is species-dependent and no feature set performs well for all species, even within the same subphylum/class. Mitigating this species dependency, we show that an ensemble of classifiers reduced the classification errors for all 45 species. As the ensemble members were obtained using meaningful, and yet computationally viable feature sets, the ensembles also have a lower computational cost than individual classifiers that rely on energy stability parameters, which are of prohibitive computational cost in large scale applications. In this study, the combination of multiple pre-miRNAs feature sets and multiple learning biases enhanced the predictive accuracy of pre-miRNAs classifiers of 45 species. This is certainly a promising approach to be incorporated in miRNA discovery tools towards more accurate and less species-dependent tools. The material to reproduce the results from this paper can be downloaded from http://dx.doi.org/10.5281/zenodo.49754 .

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 3%
Canada 1 3%
Unknown 38 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 30%
Researcher 6 15%
Professor > Associate Professor 4 10%
Student > Master 4 10%
Student > Bachelor 3 8%
Other 4 10%
Unknown 7 18%
Readers by discipline Count As %
Computer Science 11 28%
Biochemistry, Genetics and Molecular Biology 8 20%
Agricultural and Biological Sciences 7 18%
Engineering 3 8%
Veterinary Science and Veterinary Medicine 1 3%
Other 3 8%
Unknown 7 18%
Attention Score in Context

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 05 June 2016.
All research outputs
#12,959,346
of 22,875,477 outputs
Outputs from BMC Bioinformatics
#3,796
of 7,297 outputs
Outputs of similar age
#166,399
of 338,291 outputs
Outputs of similar age from BMC Bioinformatics
#45
of 94 outputs
Altmetric has tracked 22,875,477 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,297 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. 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 338,291 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 50% of its contemporaries.
We're also able to compare this research output to 94 others from the same source and published within six weeks on either side of this one. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.