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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Using Biological Pathway Data with Paxtools
|
|---|---|
| Published in |
PLoS Computational Biology, September 2013
|
| DOI | 10.1371/journal.pcbi.1003194 |
| Pubmed ID | |
| Authors |
Emek Demir, Özgün Babur, Igor Rodchenkov, Bülent Arman Aksoy, Ken I. Fukuda, Benjamin Gross, Onur Selçuk Sümer, Gary D. Bader, Chris Sander |
| Abstract |
A rapidly growing corpus of formal, computable pathway information can be used to answer important biological questions including finding non-trivial connections between cellular processes, identifying significantly altered portions of the cellular network in a disease state and building predictive models that can be used for precision medicine. Due to its complexity and fragmented nature, however, working with pathway data is still difficult. We present Paxtools, a Java library that contains algorithms, software components and converters for biological pathways represented in the standard BioPAX language. Paxtools allows scientists to focus on their scientific problem by removing technical barriers to access and analyse pathway information. Paxtools can run on any platform that has a Java Runtime Environment and was tested on most modern operating systems. Paxtools is open source and is available under the Lesser GNU public license (LGPL), which allows users to freely use the code in their software systems with a requirement for attribution. Source code for the current release (4.2.0) can be found in Software S1. A detailed manual for obtaining and using Paxtools can be found in Protocol S1. The latest sources and release bundles can be obtained from biopax.org/paxtools. |
X Demographics
The data shown below were collected from the profiles of 20 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 | 4 | 20% |
| India | 2 | 10% |
| United Kingdom | 1 | 5% |
| Japan | 1 | 5% |
| Chile | 1 | 5% |
| Canada | 1 | 5% |
| France | 1 | 5% |
| Unknown | 9 | 45% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 10 | 50% |
| Members of the public | 8 | 40% |
| Science communicators (journalists, bloggers, editors) | 2 | 10% |
Mendeley readers
The data shown below were compiled from readership statistics for 116 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 7 | 6% |
| United Kingdom | 5 | 4% |
| Germany | 2 | 2% |
| France | 2 | 2% |
| Spain | 2 | 2% |
| Brazil | 1 | <1% |
| Canada | 1 | <1% |
| Mexico | 1 | <1% |
| Korea, Republic of | 1 | <1% |
| Other | 2 | 2% |
| Unknown | 92 | 79% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 39 | 34% |
| Student > Ph. D. Student | 37 | 32% |
| Other | 7 | 6% |
| Student > Master | 7 | 6% |
| Professor > Associate Professor | 6 | 5% |
| Other | 12 | 10% |
| Unknown | 8 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 49 | 42% |
| Biochemistry, Genetics and Molecular Biology | 23 | 20% |
| Computer Science | 19 | 16% |
| Medicine and Dentistry | 6 | 5% |
| Pharmacology, Toxicology and Pharmaceutical Science | 2 | 2% |
| Other | 7 | 6% |
| Unknown | 10 | 9% |
Attention Score in Context
This research output has an Altmetric Attention Score of 21. 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 14 December 2017.
All research outputs
#1,766,903
of 25,373,627 outputs
Outputs from PLoS Computational Biology
#1,521
of 8,960 outputs
Outputs of similar age
#15,546
of 213,334 outputs
Outputs of similar age from PLoS Computational Biology
#15
of 119 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,960 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has done well, scoring higher than 83% 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 213,334 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 119 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.