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iTools: A Framework for Classification, Categorization and Integration of Computational Biology Resources

Overview of attention for article published in PLOS ONE, May 2008
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  • Good Attention Score compared to outputs of the same age (67th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
1 X user
wikipedia
2 Wikipedia pages

Citations

dimensions_citation
29 Dimensions

Readers on

mendeley
110 Mendeley
citeulike
14 CiteULike
connotea
6 Connotea
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Title
iTools: A Framework for Classification, Categorization and Integration of Computational Biology Resources
Published in
PLOS ONE, May 2008
DOI 10.1371/journal.pone.0002265
Pubmed ID
Authors

Ivo D. Dinov, Daniel Rubin, William Lorensen, Jonathan Dugan, Jeff Ma, Shawn Murphy, Beth Kirschner, William Bug, Michael Sherman, Aris Floratos, David Kennedy, H. V. Jagadish, Jeanette Schmidt, Brian Athey, Andrea Califano, Mark Musen, Russ Altman, Ron Kikinis, Isaac Kohane, Scott Delp, D. Stott Parker, Arthur W. Toga

Abstract

The advancement of the computational biology field hinges on progress in three fundamental directions--the development of new computational algorithms, the availability of informatics resource management infrastructures and the capability of tools to interoperate and synergize. There is an explosion in algorithms and tools for computational biology, which makes it difficult for biologists to find, compare and integrate such resources. We describe a new infrastructure, iTools, for managing the query, traversal and comparison of diverse computational biology resources. Specifically, iTools stores information about three types of resources--data, software tools and web-services. The iTools design, implementation and resource meta-data content reflect the broad research, computational, applied and scientific expertise available at the seven National Centers for Biomedical Computing. iTools provides a system for classification, categorization and integration of different computational biology resources across space-and-time scales, biomedical problems, computational infrastructures and mathematical foundations. A large number of resources are already iTools-accessible to the community and this infrastructure is rapidly growing. iTools includes human and machine interfaces to its resource meta-data repository. Investigators or computer programs may utilize these interfaces to search, compare, expand, revise and mine meta-data descriptions of existent computational biology resources. We propose two ways to browse and display the iTools dynamic collection of resources. The first one is based on an ontology of computational biology resources, and the second one is derived from hyperbolic projections of manifolds or complex structures onto planar discs. iTools is an open source project both in terms of the source code development as well as its meta-data content. iTools employs a decentralized, portable, scalable and lightweight framework for long-term resource management. We demonstrate several applications of iTools as a framework for integrated bioinformatics. iTools and the complete details about its specifications, usage and interfaces are available at the iTools web page http://iTools.ccb.ucla.edu.

X Demographics

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.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 110 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 11 10%
United Kingdom 3 3%
Mexico 3 3%
Spain 2 2%
Turkey 1 <1%
Switzerland 1 <1%
Afghanistan 1 <1%
Colombia 1 <1%
Unknown 87 79%

Demographic breakdown

Readers by professional status Count As %
Researcher 32 29%
Student > Master 12 11%
Other 11 10%
Professor 11 10%
Student > Ph. D. Student 11 10%
Other 27 25%
Unknown 6 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 48 44%
Medicine and Dentistry 14 13%
Computer Science 11 10%
Engineering 9 8%
Biochemistry, Genetics and Molecular Biology 7 6%
Other 18 16%
Unknown 3 3%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 13 November 2015.
All research outputs
#6,413,935
of 22,788,370 outputs
Outputs from PLOS ONE
#77,136
of 194,531 outputs
Outputs of similar age
#25,381
of 83,241 outputs
Outputs of similar age from PLOS ONE
#199
of 365 outputs
Altmetric has tracked 22,788,370 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 194,531 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.1. This one has gotten more attention than average, scoring higher than 59% 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 83,241 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.
We're also able to compare this research output to 365 others from the same source and published within six weeks on either side of this one. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.