| Title |
Prediction of disease genes using tissue-specified gene-gene network
|
|---|---|
| Published in |
BMC Systems Biology, October 2014
|
| DOI | 10.1186/1752-0509-8-s3-s3 |
| Pubmed ID | |
| Authors |
Gamage Upeksha Ganegoda, JianXin Wang, Fang-Xiang Wu, Min Li |
| Abstract |
Tissue specificity is an important aspect of many genetic diseases in the context of genetic disorders as the disorder affects only few tissues. Therefore tissue specificity is important in identifying disease-gene associations. Hence this paper seeks to discuss the impact of using tissue specificity in predicting new disease-gene associations and how to use tissue specificity along with phenotype information for a particular disease. |
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 % |
|---|---|---|
| United States | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 2 | 6% |
| Japan | 1 | 3% |
| United States | 1 | 3% |
| Unknown | 32 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 8 | 22% |
| Student > Ph. D. Student | 7 | 19% |
| Student > Master | 6 | 17% |
| Student > Doctoral Student | 4 | 11% |
| Student > Postgraduate | 3 | 8% |
| Other | 5 | 14% |
| Unknown | 3 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 11 | 31% |
| Biochemistry, Genetics and Molecular Biology | 9 | 25% |
| Computer Science | 9 | 25% |
| Physics and Astronomy | 2 | 6% |
| Medicine and Dentistry | 1 | 3% |
| Other | 0 | 0% |
| Unknown | 4 | 11% |
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 25 June 2015.
All research outputs
#14,788,263
of 22,768,097 outputs
Outputs from BMC Systems Biology
#600
of 1,142 outputs
Outputs of similar age
#143,845
of 260,342 outputs
Outputs of similar age from BMC Systems Biology
#20
of 31 outputs
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So far Altmetric has tracked 1,142 research outputs from this source. They receive a mean Attention Score of 3.6. This one is in the 43rd percentile – i.e., 43% of its peers scored the same or lower than it.
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We're also able to compare this research output to 31 others from the same source and published within six weeks on either side of this one. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.