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Drug combinatorics and side effect estimation on the signed human drug-target network

Overview of attention for article published in BMC Systems Biology, August 2016
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Title
Drug combinatorics and side effect estimation on the signed human drug-target network
Published in
BMC Systems Biology, August 2016
DOI 10.1186/s12918-016-0326-8
Pubmed ID
Authors

Núria Ballber Torres, Claudio Altafini

Abstract

The mode of action of a drug on its targets can often be classified as being positive (activator, potentiator, agonist, etc.) or negative (inhibitor, blocker, antagonist, etc.). The signed edges of a drug-target network can be used to investigate the combined mechanisms of action of multiple drugs on the ensemble of common targets. In this paper it is shown that for the signed human drug-target network the majority of drug pairs tend to have synergistic effects on the common targets, i.e., drug pairs tend to have modes of action with the same sign on most of the shared targets, especially for the principal pharmacological targets of a drug. Methods are proposed to compute this synergism, as well as to estimate the influence of the drugs on the side effect of another drug. Enriching a drug-target network with information of functional nature like the sign of the interactions allows to explore in a systematic way a series of network properties of key importance in the context of computational drug combinatorics.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Hungary 1 4%
Unknown 25 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 23%
Student > Master 4 15%
Student > Bachelor 2 8%
Professor 2 8%
Researcher 2 8%
Other 4 15%
Unknown 6 23%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 6 23%
Agricultural and Biological Sciences 3 12%
Chemistry 3 12%
Computer Science 2 8%
Pharmacology, Toxicology and Pharmaceutical Science 1 4%
Other 3 12%
Unknown 8 31%