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Attention Score in Context
| Title |
Integration of multiple networks and pathways identifies cancer driver genes in pan-cancer analysis
|
|---|---|
| Published in |
BMC Genomics, January 2018
|
| DOI | 10.1186/s12864-017-4423-x |
| Pubmed ID | |
| Authors |
Claudia Cava, Gloria Bertoli, Antonio Colaprico, Catharina Olsen, Gianluca Bontempi, Isabella Castiglioni |
| Abstract |
Modern high-throughput genomic technologies represent a comprehensive hallmark of molecular changes in pan-cancer studies. Although different cancer gene signatures have been revealed, the mechanism of tumourigenesis has yet to be completely understood. Pathways and networks are important tools to explain the role of genes in functional genomic studies. However, few methods consider the functional non-equal roles of genes in pathways and the complex gene-gene interactions in a network. We present a novel method in pan-cancer analysis that identifies de-regulated genes with a functional role by integrating pathway and network data. A pan-cancer analysis of 7158 tumour/normal samples from 16 cancer types identified 895 genes with a central role in pathways and de-regulated in cancer. Comparing our approach with 15 current tools that identify cancer driver genes, we found that 35.6% of the 895 genes identified by our method have been found as cancer driver genes with at least 2/15 tools. Finally, we applied a machine learning algorithm on 16 independent GEO cancer datasets to validate the diagnostic role of cancer driver genes for each cancer. We obtained a list of the top-ten cancer driver genes for each cancer considered in this study. Our analysis 1) confirmed that there are several known cancer driver genes in common among different types of cancer, 2) highlighted that cancer driver genes are able to regulate crucial pathways. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Italy | 1 | 25% |
| Unknown | 3 | 75% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 75% |
| Scientists | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 73 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 16 | 22% |
| Student > Ph. D. Student | 13 | 18% |
| Student > Bachelor | 8 | 11% |
| Student > Master | 7 | 10% |
| Student > Postgraduate | 4 | 5% |
| Other | 9 | 12% |
| Unknown | 16 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 20 | 27% |
| Agricultural and Biological Sciences | 9 | 12% |
| Computer Science | 9 | 12% |
| Medicine and Dentistry | 7 | 10% |
| Pharmacology, Toxicology and Pharmaceutical Science | 2 | 3% |
| Other | 9 | 12% |
| Unknown | 17 | 23% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 20 October 2023.
All research outputs
#6,062,706
of 24,717,692 outputs
Outputs from BMC Genomics
#2,284
of 11,055 outputs
Outputs of similar age
#112,727
of 452,879 outputs
Outputs of similar age from BMC Genomics
#49
of 219 outputs
Altmetric has tracked 24,717,692 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,055 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done well, scoring higher than 79% 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 452,879 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 75% of its contemporaries.
We're also able to compare this research output to 219 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.