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A clustering-based approach for efficient identification of microRNA combinatorial biomarkers

Overview of attention for article published in BMC Genomics, March 2017
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Title
A clustering-based approach for efficient identification of microRNA combinatorial biomarkers
Published in
BMC Genomics, March 2017
DOI 10.1186/s12864-017-3498-8
Pubmed ID
Authors

Yang Yang, Ning Huang, Luning Hao, Wei Kong

Abstract

MicroRNAs (miRNAs) have great potential serving as tumor biomarkers and therapeutic targets. As the rapid development of high-throughput experimental technology, gene expression experiments have become more and more specialized and diversified. The complex data structure has brought great challenge for the identification of biomarkers. In the meantime, current statistical and machine learning methods for detecting biomarkers have the problem of low reliability and biased criteria. This study aims to select combinatorial miRNA biomarkers, which have higher sensitivity and specificity than single-gene biomarkers. In order to avoid exhaustive search and redundant information, miRNAs are firstly clustered, then the combinations of representative cluster members are assessed as potential biomarkers. Both the criteria for the partition of clusters and selection of representative members are based on Fisher linear discriminant analysis (FDA). The FDA-based criterion has been demonstrated to be superior to three other criteria in selecting representative members, and also good at refining clusters. In the comparison with eight common feature selection methods, this clustering-based method performs the best with regard to the discriminative ability of selected biomarkers. Our experimental results demonstrate that the clustering-based method can identify microRNA combinatorial biomarkers with high accuracy and efficiency. Our method and data are available to the public upon request.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 30 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 4 13%
Student > Bachelor 4 13%
Researcher 3 10%
Student > Ph. D. Student 3 10%
Student > Doctoral Student 1 3%
Other 4 13%
Unknown 11 37%
Readers by discipline Count As %
Medicine and Dentistry 4 13%
Biochemistry, Genetics and Molecular Biology 4 13%
Computer Science 3 10%
Chemistry 2 7%
Environmental Science 1 3%
Other 4 13%
Unknown 12 40%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 01 April 2017.
All research outputs
#20,412,387
of 22,962,258 outputs
Outputs from BMC Genomics
#9,311
of 10,686 outputs
Outputs of similar age
#268,642
of 307,953 outputs
Outputs of similar age from BMC Genomics
#163
of 200 outputs
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