You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
| Title |
MGOGP: a gene module-based heuristic algorithm for cancer-related gene prioritization
|
|---|---|
| Published in |
BMC Bioinformatics, June 2018
|
| DOI | 10.1186/s12859-018-2216-0 |
| Pubmed ID | |
| Authors |
Lingtao Su, Guixia Liu, Tian Bai, Xiangyu Meng, Qingshan Ma |
| Abstract |
Prioritizing genes according to their associations with a cancer allows researchers to explore genes in more informed ways. By far, Gene-centric or network-centric gene prioritization methods are predominated. Genes and their protein products carry out cellular processes in the context of functional modules. Dysfunctional gene modules have been previously reported to have associations with cancer. However, gene module information has seldom been considered in cancer-related gene prioritization. In this study, we propose a novel method, MGOGP (Module and Gene Ontology-based Gene Prioritization), for cancer-related gene prioritization. Different from other methods, MGOGP ranks genes considering information of both individual genes and their affiliated modules, and utilize Gene Ontology (GO) based fuzzy measure value as well as known cancer-related genes as heuristics. The performance of the proposed method is comprehensively validated by using both breast cancer and prostate cancer datasets, and by comparison with other methods. Results show that MGOGP outperforms other methods, and successfully prioritizes more genes with literature confirmed evidence. This work will aid researchers in the understanding of the genetic architecture of complex diseases, and improve the accuracy of diagnosis and the effectiveness of therapy. |
X Demographics
The data shown below were collected from the profiles of 2 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 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Scientists | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 20 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 3 | 15% |
| Student > Master | 2 | 10% |
| Student > Ph. D. Student | 1 | 5% |
| Researcher | 1 | 5% |
| Professor > Associate Professor | 1 | 5% |
| Other | 1 | 5% |
| Unknown | 11 | 55% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 2 | 10% |
| Medicine and Dentistry | 2 | 10% |
| Linguistics | 1 | 5% |
| Biochemistry, Genetics and Molecular Biology | 1 | 5% |
| Mathematics | 1 | 5% |
| Other | 1 | 5% |
| Unknown | 12 | 60% |
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 09 May 2019.
All research outputs
#18,637,483
of 23,088,369 outputs
Outputs from BMC Bioinformatics
#6,360
of 7,325 outputs
Outputs of similar age
#254,906
of 329,782 outputs
Outputs of similar age from BMC Bioinformatics
#79
of 107 outputs
Altmetric has tracked 23,088,369 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,325 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 5th percentile – i.e., 5% 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 329,782 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 107 others from the same source and published within six weeks on either side of this one. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.