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Using Biological Pathway Data with Paxtools

Overview of attention for article published in PLoS Computational Biology, September 2013
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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.

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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%