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| Title |
A decision analysis model for KEGG pathway analysis
|
|---|---|
| Published in |
BMC Bioinformatics, October 2016
|
| DOI | 10.1186/s12859-016-1285-1 |
| Pubmed ID | |
| Authors |
Junli Du, Manlin Li, Zhifa Yuan, Mancai Guo, Jiuzhou Song, Xiaozhen Xie, Yulin Chen |
| Abstract |
The knowledge base-driven pathway analysis is becoming the first choice for many investigators, in that it not only can reduce the complexity of functional analysis by grouping thousands of genes into just several hundred pathways, but also can increase the explanatory power for the experiment by identifying active pathways in different conditions. However, current approaches are designed to analyze a biological system assuming that each pathway is independent of the other pathways. A decision analysis model is developed in this article that accounts for dependence among pathways in time-course experiments and multiple treatments experiments. This model introduces a decision coefficient-a designed index, to identify the most relevant pathways in a given experiment by taking into account not only the direct determination factor of each Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway itself, but also the indirect determination factors from its related pathways. Meanwhile, the direct and indirect determination factors of each pathway are employed to demonstrate the regulation mechanisms among KEGG pathways, and the sign of decision coefficient can be used to preliminarily estimate the impact direction of each KEGG pathway. The simulation study of decision analysis demonstrated the application of decision analysis model for KEGG pathway analysis. A microarray dataset from bovine mammary tissue over entire lactation cycle was used to further illustrate our strategy. The results showed that the decision analysis model can provide the promising and more biologically meaningful results. Therefore, the decision analysis model is an initial attempt of optimizing pathway analysis methodology. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| China | 1 | 33% |
| Spain | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 67% |
| Members of the public | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 114 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 2% |
| Sweden | 1 | <1% |
| China | 1 | <1% |
| Singapore | 1 | <1% |
| Unknown | 109 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 24 | 21% |
| Student > Master | 16 | 14% |
| Researcher | 15 | 13% |
| Other | 8 | 7% |
| Student > Bachelor | 7 | 6% |
| Other | 14 | 12% |
| Unknown | 30 | 26% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 32 | 28% |
| Agricultural and Biological Sciences | 18 | 16% |
| Computer Science | 11 | 10% |
| Medicine and Dentistry | 5 | 4% |
| Pharmacology, Toxicology and Pharmaceutical Science | 4 | 4% |
| Other | 10 | 9% |
| Unknown | 34 | 30% |
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 23 November 2017.
All research outputs
#14,864,294
of 22,893,031 outputs
Outputs from BMC Bioinformatics
#5,058
of 7,299 outputs
Outputs of similar age
#192,109
of 319,894 outputs
Outputs of similar age from BMC Bioinformatics
#76
of 132 outputs
Altmetric has tracked 22,893,031 research outputs across all sources so far. This one is in the 33rd percentile – i.e., 33% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,299 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 26th percentile – i.e., 26% 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 319,894 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 132 others from the same source and published within six weeks on either side of this one. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.