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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Predicting tissue specific transcription factor binding sites
|
|---|---|
| Published in |
BMC Genomics, November 2013
|
| DOI | 10.1186/1471-2164-14-796 |
| Pubmed ID | |
| Authors |
Shan Zhong, Xin He, Ziv Bar-Joseph |
| Abstract |
Studies of gene regulation often utilize genome-wide predictions of transcription factor (TF) binding sites. Most existing prediction methods are based on sequence information alone, ignoring biological contexts such as developmental stages and tissue types. Experimental methods to study in vivo binding, including ChIP-chip and ChIP-seq, can only study one transcription factor in a single cell type and under a specific condition in each experiment, and therefore cannot scale to determine the full set of regulatory interactions in mammalian transcriptional regulatory networks. |
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 % |
|---|---|---|
| Scientists | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 86 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Italy | 2 | 2% |
| United States | 2 | 2% |
| Spain | 1 | 1% |
| Unknown | 81 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 26 | 30% |
| Researcher | 26 | 30% |
| Student > Master | 10 | 12% |
| Student > Bachelor | 7 | 8% |
| Student > Postgraduate | 4 | 5% |
| Other | 10 | 12% |
| Unknown | 3 | 3% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 42 | 49% |
| Biochemistry, Genetics and Molecular Biology | 20 | 23% |
| Computer Science | 6 | 7% |
| Engineering | 3 | 3% |
| Business, Management and Accounting | 1 | 1% |
| Other | 5 | 6% |
| Unknown | 9 | 10% |
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 18 November 2013.
All research outputs
#15,285,728
of 22,731,677 outputs
Outputs from BMC Genomics
#6,667
of 10,628 outputs
Outputs of similar age
#129,803
of 211,390 outputs
Outputs of similar age from BMC Genomics
#72
of 156 outputs
Altmetric has tracked 22,731,677 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,628 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 29th percentile – i.e., 29% 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 211,390 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 156 others from the same source and published within six weeks on either side of this one. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.