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Missing value imputation for microRNA expression data by using a GO-based similarity measure

Overview of attention for article published in BMC Bioinformatics, January 2016
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Title
Missing value imputation for microRNA expression data by using a GO-based similarity measure
Published in
BMC Bioinformatics, January 2016
DOI 10.1186/s12859-015-0853-0
Pubmed ID
Authors

Yang, Zhuangdi Xu, Dandan Song

Abstract

Missing values are commonly present in microarray data profiles. Instead of discarding genes or samples with incomplete expression level, missing values need to be properly imputed for accurate data analysis. The imputation methods can be roughly categorized as expression level-based and domain knowledge-based. The first type of methods only rely on expression data without the help of external data sources, while the second type incorporates available domain knowledge into expression data to improve imputation accuracy. In recent years, microRNA (miRNA) microarray has been largely developed and used for identifying miRNA biomarkers in complex human disease studies. Similar to mRNA profiles, miRNA expression profiles with missing values can be treated with the existing imputation methods. However, the domain knowledge-based methods are hard to be applied due to the lack of direct functional annotation for miRNAs. With the rapid accumulation of miRNA microarray data, it is increasingly needed to develop domain knowledge-based imputation algorithms specific to miRNA expression profiles to improve the quality of miRNA data analysis. We connect miRNAs with domain knowledge of Gene Ontology (GO) via their target genes, and define miRNA functional similarity based on the semantic similarity of GO terms in GO graphs. A new measure combining miRNA functional similarity and expression similarity is used in the imputation of missing values. The new measure is tested on two miRNA microarray datasets from breast cancer research and achieves improved performance compared with the expression-based method on both datasets. The experimental results demonstrate that the biological domain knowledge can benefit the estimation of missing values in miRNA profiles as well as mRNA profiles. Especially, functional similarity defined by GO terms annotated for the target genes of miRNAs can be useful complementary information for the expression-based method to improve the imputation accuracy of miRNA array data. Our method and data are available to the public upon request.

Twitter Demographics

The data shown below were collected from the profiles of 6 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 1 4%
Unknown 22 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 26%
Student > Ph. D. Student 4 17%
Student > Bachelor 3 13%
Student > Doctoral Student 2 9%
Student > Master 2 9%
Other 4 17%
Unknown 2 9%
Readers by discipline Count As %
Computer Science 7 30%
Agricultural and Biological Sciences 4 17%
Engineering 3 13%
Mathematics 2 9%
Biochemistry, Genetics and Molecular Biology 1 4%
Other 2 9%
Unknown 4 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 30 January 2016.
All research outputs
#5,471,347
of 10,444,782 outputs
Outputs from BMC Bioinformatics
#2,462
of 4,169 outputs
Outputs of similar age
#137,813
of 329,881 outputs
Outputs of similar age from BMC Bioinformatics
#80
of 133 outputs
Altmetric has tracked 10,444,782 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 4,169 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 38th percentile – i.e., 38% 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 329,881 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 56% of its contemporaries.
We're also able to compare this research output to 133 others from the same source and published within six weeks on either side of this one. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.