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Hierarchical structural component modeling of microRNA-mRNA integration analysis

Overview of attention for article published in BMC Bioinformatics, May 2018
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1 tweeter

Citations

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Title
Hierarchical structural component modeling of microRNA-mRNA integration analysis
Published in
BMC Bioinformatics, May 2018
DOI 10.1186/s12859-018-2070-0
Pubmed ID
Authors

Yongkang Kim, Sungyoung Lee, Sungkyoung Choi, Jin-Young Jang, Taesung Park

Abstract

Identification of multi-markers is one of the most challenging issues in personalized medicine era. Nowadays, many different types of omics data are generated from the same subject. Although many methods endeavor to identify candidate markers, for each type of omics data, few or none can facilitate such identification. It is well known that microRNAs affect phenotypes only indirectly, through regulating mRNA expression and/or protein translation. Toward addressing this issue, we suggest a hierarchical structured component analysis of microRNA-mRNA integration ("HisCoM-mimi") model that accounts for this biological relationship, to efficiently study and identify such integrated markers. In simulation studies, HisCoM-mimi showed the better performance than the other three methods. Also, in real data analysis, HisCoM-mimi successfully identified more gives more informative miRNA-mRNA integration sets relationships for pancreatic ductal adenocarcinoma (PDAC) diagnosis, compared to the other methods. As exemplified by an application to pancreatic cancer data, our proposed model effectively identified integrated miRNA/target mRNA pairs as markers for early diagnosis, providing a much broader biological interpretation.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 3 25%
Researcher 3 25%
Student > Ph. D. Student 2 17%
Other 2 17%
Student > Master 1 8%
Other 1 8%
Readers by discipline Count As %
Medicine and Dentistry 6 50%
Biochemistry, Genetics and Molecular Biology 3 25%
Agricultural and Biological Sciences 1 8%
Engineering 1 8%
Unknown 1 8%

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 13 May 2018.
All research outputs
#11,493,030
of 12,931,128 outputs
Outputs from BMC Bioinformatics
#4,451
of 4,820 outputs
Outputs of similar age
#234,124
of 270,097 outputs
Outputs of similar age from BMC Bioinformatics
#10
of 21 outputs
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