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Network based elucidation of drug response: from modulators to targets

Overview of attention for article published in BMC Systems Biology, December 2013
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2 X users

Citations

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45 Dimensions

Readers on

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154 Mendeley
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4 CiteULike
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Title
Network based elucidation of drug response: from modulators to targets
Published in
BMC Systems Biology, December 2013
DOI 10.1186/1752-0509-7-139
Pubmed ID
Authors

Francesco Iorio, Julio Saez-Rodriguez, Diego di Bernardo

Abstract

: Network-based drug discovery aims at harnessing the power of networks to investigate the mechanism of action of existing drugs, or new molecules, in order to identify innovative therapeutic treatments. In this review, we describe some of the most recent advances in the field of network pharmacology, starting with approaches relying on computational models of transcriptional networks, then moving to protein and signaling network models and concluding with "drug networks". These networks are derived from different sources of experimental data, or literature-based analysis, and provide a complementary view of drug mode of action. Molecular and drug networks are powerful integrated computational and experimental approaches that will likely speed up and improve the drug discovery process, once fully integrated into the academic and industrial drug discovery pipeline.

X Demographics

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.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 154 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
India 2 1%
Spain 2 1%
Brazil 1 <1%
Sweden 1 <1%
Singapore 1 <1%
Austria 1 <1%
Iran, Islamic Republic of 1 <1%
United States 1 <1%
Unknown 144 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 49 32%
Researcher 34 22%
Student > Master 17 11%
Student > Bachelor 10 6%
Professor > Associate Professor 9 6%
Other 24 16%
Unknown 11 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 49 32%
Biochemistry, Genetics and Molecular Biology 31 20%
Computer Science 18 12%
Engineering 11 7%
Chemistry 9 6%
Other 21 14%
Unknown 15 10%
Attention Score in Context

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 02 April 2014.
All research outputs
#17,706,524
of 22,736,112 outputs
Outputs from BMC Systems Biology
#770
of 1,142 outputs
Outputs of similar age
#223,357
of 307,365 outputs
Outputs of similar age from BMC Systems Biology
#41
of 61 outputs
Altmetric has tracked 22,736,112 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,142 research outputs from this source. They receive a mean Attention Score of 3.6. This one is in the 27th percentile – i.e., 27% 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 307,365 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 61 others from the same source and published within six weeks on either side of this one. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.