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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Learning contextual gene set interaction networks of cancer with condition specificity
|
|---|---|
| Published in |
BMC Genomics, February 2013
|
| DOI | 10.1186/1471-2164-14-110 |
| Pubmed ID | |
| Authors |
Sungwon Jung, Michael Verdicchio, Jeff Kiefer, Daniel Von Hoff, Michael Berens, Michael Bittner, Seungchan Kim |
| Abstract |
Identifying similarities and differences in the molecular constitutions of various types of cancer is one of the key challenges in cancer research. The appearances of a cancer depend on complex molecular interactions, including gene regulatory networks and gene-environment interactions. This complexity makes it challenging to decipher the molecular origin of the cancer. In recent years, many studies reported methods to uncover heterogeneous depictions of complex cancers, which are often categorized into different subtypes. The challenge is to identify diverse molecular contexts within a cancer, to relate them to different subtypes, and to learn underlying molecular interactions specific to molecular contexts so that we can recommend context-specific treatment to patients. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 1 | 5% |
| Unknown | 20 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 4 | 19% |
| Student > Master | 4 | 19% |
| Researcher | 3 | 14% |
| Student > Doctoral Student | 2 | 10% |
| Student > Ph. D. Student | 2 | 10% |
| Other | 5 | 24% |
| Unknown | 1 | 5% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 5 | 24% |
| Biochemistry, Genetics and Molecular Biology | 4 | 19% |
| Agricultural and Biological Sciences | 4 | 19% |
| Computer Science | 3 | 14% |
| Mathematics | 1 | 5% |
| Other | 3 | 14% |
| Unknown | 1 | 5% |
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 20 February 2013.
All research outputs
#18,329,207
of 22,696,971 outputs
Outputs from BMC Genomics
#8,146
of 10,616 outputs
Outputs of similar age
#146,839
of 193,023 outputs
Outputs of similar age from BMC Genomics
#85
of 122 outputs
Altmetric has tracked 22,696,971 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 10,616 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 12th percentile – i.e., 12% 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 193,023 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 122 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.