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Mendeley readers
Attention Score in Context
| Title |
Efficient and biologically relevant consensus strategy for Parkinson’s disease gene prioritization
|
|---|---|
| Published in |
BMC Medical Genomics, March 2016
|
| DOI | 10.1186/s12920-016-0173-x |
| Pubmed ID | |
| Authors |
Maykel Cruz-Monteagudo, Fernanda Borges, Cesar Paz-y-Miño, M. Natália D. S. Cordeiro, Irene Rebelo, Yunierkis Perez-Castillo, Aliuska Morales Helguera, Aminael Sánchez-Rodríguez, Eduardo Tejera |
| Abstract |
The systemic information enclosed in microarray data encodes relevant clues to overcome the poorly understood combination of genetic and environmental factors in Parkinson's disease (PD), which represents the major obstacle to understand its pathogenesis and to develop disease-modifying therapeutics. While several gene prioritization approaches have been proposed, none dominate over the rest. Instead, hybrid approaches seem to outperform individual approaches. A consensus strategy is proposed for PD related gene prioritization from mRNA microarray data based on the combination of three independent prioritization approaches: Limma, machine learning, and weighted gene co-expression networks. The consensus strategy outperformed the individual approaches in terms of statistical significance, overall enrichment and early recognition ability. In addition to a significant biological relevance, the set of 50 genes prioritized exhibited an excellent early recognition ability (6 of the top 10 genes are directly associated with PD). 40 % of the prioritized genes were previously associated with PD including well-known PD related genes such as SLC18A2, TH or DRD2. Eight genes (CCNH, DLK1, PCDH8, SLIT1, DLD, PBX1, INSM1, and BMI1) were found to be significantly associated to biological process affected in PD, representing potentially novel PD biomarkers or therapeutic targets. Additionally, several metrics of standard use in chemoinformatics are proposed to evaluate the early recognition ability of gene prioritization tools. The proposed consensus strategy represents an efficient and biologically relevant approach for gene prioritization tasks providing a valuable decision-making tool for the study of PD pathogenesis and the development of disease-modifying PD therapeutics. |
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% |
| Practitioners (doctors, other healthcare professionals) | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 82 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 1% |
| Belgium | 1 | 1% |
| Unknown | 80 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 15 | 18% |
| Student > Ph. D. Student | 12 | 15% |
| Student > Master | 8 | 10% |
| Student > Bachelor | 7 | 9% |
| Professor | 5 | 6% |
| Other | 14 | 17% |
| Unknown | 21 | 26% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 14 | 17% |
| Agricultural and Biological Sciences | 11 | 13% |
| Medicine and Dentistry | 7 | 9% |
| Computer Science | 6 | 7% |
| Neuroscience | 6 | 7% |
| Other | 16 | 20% |
| Unknown | 22 | 27% |
Attention Score in Context
This research output has an Altmetric Attention Score of 11. 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 01 October 2016.
All research outputs
#2,994,553
of 23,881,329 outputs
Outputs from BMC Medical Genomics
#116
of 1,268 outputs
Outputs of similar age
#47,536
of 302,680 outputs
Outputs of similar age from BMC Medical Genomics
#2
of 10 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,268 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done particularly well, scoring higher than 91% of its peers.
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 302,680 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 84% of its contemporaries.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 8 of them.