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Attention Score in Context
| Title |
Prioritizing biological pathways by recognizing context in time-series gene expression data
|
|---|---|
| Published in |
BMC Bioinformatics, December 2016
|
| DOI | 10.1186/s12859-016-1335-8 |
| Pubmed ID | |
| Authors |
Jusang Lee, Kyuri Jo, Sunwon Lee, Jaewoo Kang, Sun Kim |
| Abstract |
The primary goal of pathway analysis using transcriptome data is to find significantly perturbed pathways. However, pathway analysis is not always successful in identifying pathways that are truly relevant to the context under study. A major reason for this difficulty is that a single gene is involved in multiple pathways. In the KEGG pathway database, there are 146 genes, each of which is involved in more than 20 pathways. Thus activation of even a single gene will result in activation of many pathways. This complex relationship often makes the pathway analysis very difficult. While we need much more powerful pathway analysis methods, a readily available alternative way is to incorporate the literature information. In this study, we propose a novel approach for prioritizing pathways by combining results from both pathway analysis tools and literature information. The basic idea is as follows. Whenever there are enough articles that provide evidence on which pathways are relevant to the context, we can be assured that the pathways are indeed related to the context, which is termed as relevance in this paper. However, if there are few or no articles reported, then we should rely on the results from the pathway analysis tools, which is termed as significance in this paper. We realized this concept as an algorithm by introducing Context Score and Impact Score and then combining the two into a single score. Our method ranked truly relevant pathways significantly higher than existing pathway analysis tools in experiments with two data sets. Our novel framework was implemented as ContextTRAP by utilizing two existing tools, TRAP and BEST. ContextTRAP will be a useful tool for the pathway based analysis of gene expression data since the user can specify the context of the biological experiment in a set of keywords. The web version of ContextTRAP is available at http://biohealth.snu.ac.kr/software/contextTRAP . |
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 % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 34 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 8 | 24% |
| Student > Master | 6 | 18% |
| Researcher | 5 | 15% |
| Student > Bachelor | 4 | 12% |
| Professor | 2 | 6% |
| Other | 5 | 15% |
| Unknown | 4 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 11 | 32% |
| Biochemistry, Genetics and Molecular Biology | 9 | 26% |
| Agricultural and Biological Sciences | 2 | 6% |
| Engineering | 2 | 6% |
| Physics and Astronomy | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 8 | 24% |
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 06 February 2017.
All research outputs
#20,402,251
of 22,952,268 outputs
Outputs from BMC Bioinformatics
#6,882
of 7,308 outputs
Outputs of similar age
#354,955
of 420,462 outputs
Outputs of similar age from BMC Bioinformatics
#111
of 136 outputs
Altmetric has tracked 22,952,268 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 7,308 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 136 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.