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Mendeley readers
Attention Score in Context
| 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. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 14 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 3 | 21% |
| Researcher | 3 | 21% |
| Student > Ph. D. Student | 3 | 21% |
| Other | 2 | 14% |
| Student > Master | 1 | 7% |
| Other | 1 | 7% |
| Unknown | 1 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 6 | 43% |
| Biochemistry, Genetics and Molecular Biology | 3 | 21% |
| Computer Science | 1 | 7% |
| Agricultural and Biological Sciences | 1 | 7% |
| Engineering | 1 | 7% |
| Other | 0 | 0% |
| Unknown | 2 | 14% |
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
#20,488,697
of 23,051,185 outputs
Outputs from BMC Bioinformatics
#6,895
of 7,319 outputs
Outputs of similar age
#288,388
of 327,709 outputs
Outputs of similar age from BMC Bioinformatics
#92
of 109 outputs
Altmetric has tracked 23,051,185 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,319 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 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 109 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.