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Prediction of drugs having opposite effects on disease genes in a directed network

Overview of attention for article published in BMC Systems Biology, January 2016
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Mentioned by

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3 tweeters

Citations

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19 Dimensions

Readers on

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35 Mendeley
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1 CiteULike
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Title
Prediction of drugs having opposite effects on disease genes in a directed network
Published in
BMC Systems Biology, January 2016
DOI 10.1186/s12918-015-0243-2
Pubmed ID
Authors

Hasun Yu, Sungji Choo, Junseok Park, Jinmyung Jung, Yeeok Kang, Doheon Lee

Abstract

Developing novel uses of approved drugs, called drug repositioning, can reduce costs and times in traditional drug development. Network-based approaches have presented promising results in this field. However, even though various types of interactions such as activation or inhibition exist in drug-target interactions and molecular pathways, most of previous network-based studies disregarded this information. We developed a novel computational method, Prediction of Drugs having Opposite effects on Disease genes (PDOD), for identifying drugs having opposite effects on altered states of disease genes. PDOD utilized drug-drug target interactions with 'effect type', an integrated directed molecular network with 'effect type' and 'effect direction', and disease genes with regulated states in disease patients. With this information, we proposed a scoring function to discover drugs likely to restore altered states of disease genes using the path from a drug to a disease through the drug-drug target interactions, shortest paths from drug targets to disease genes in molecular pathways, and disease gene-disease associations. We collected drug-drug target interactions, molecular pathways, and disease genes with their regulated states in the diseases. PDOD is applied to 898 drugs with known drug-drug target interactions and nine diseases. We compared performance of PDOD for predicting known therapeutic drug-disease associations with the previous methods. PDOD outperformed other previous approaches which do not exploit directional information in molecular network. In addition, we provide a simple web service that researchers can submit genes of interest with their altered states and will obtain drugs seeming to have opposite effects on altered states of input genes at http://gto.kaist.ac.kr/pdod/index.php/main . Our results showed that 'effect type' and 'effect direction' information in the network based approaches can be utilized to identify drugs having opposite effects on diseases. Our study can offer a novel insight into the field of network-based drug repositioning.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 3%
Luxembourg 1 3%
Unknown 33 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 29%
Student > Ph. D. Student 6 17%
Student > Bachelor 4 11%
Student > Master 4 11%
Student > Postgraduate 3 9%
Other 4 11%
Unknown 4 11%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 11 31%
Agricultural and Biological Sciences 5 14%
Medicine and Dentistry 5 14%
Computer Science 4 11%
Chemistry 2 6%
Other 3 9%
Unknown 5 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 12 January 2016.
All research outputs
#10,995,325
of 18,245,787 outputs
Outputs from BMC Systems Biology
#506
of 1,116 outputs
Outputs of similar age
#181,628
of 379,609 outputs
Outputs of similar age from BMC Systems Biology
#45
of 102 outputs
Altmetric has tracked 18,245,787 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,116 research outputs from this source. They receive a mean Attention Score of 3.5. This one is in the 49th percentile – i.e., 49% 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 379,609 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 102 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.