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MD-Miner: a network-based approach for personalized drug repositioning

Overview of attention for article published in BMC Systems Biology, October 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (84th percentile)
  • High Attention Score compared to outputs of the same age and source (94th percentile)

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23 X users

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Title
MD-Miner: a network-based approach for personalized drug repositioning
Published in
BMC Systems Biology, October 2017
DOI 10.1186/s12918-017-0462-9
Pubmed ID
Authors

Haoyang Wu, Elise Miller, Denethi Wijegunawardana, Kelly Regan, Philip R.O. Payne, Fuhai Li

Abstract

Due to advances in next generation sequencing technologies and corresponding reductions in cost, it is now attainable to investigate genome-wide gene expression and variants at a patient-level, so as to better understand and anticipate heterogeneous responses to therapy. Consequently, it is feasible to inform personalized drug treatment decisions using personal genomics data. However, these efforts are limited due to a lack of reliable computational approaches for predicting effective drugs for individual patients. The reverse gene set enrichment analysis (i.e., connectivity mapping) approach and its variants have been widely and successfully used for drug prediction. However, the performance of these methods is limited by undefined mechanism of action (MoA) of drugs and reliance on cohorts of patients rather than personalized predictions for individual patients. In this study, we have developed and evaluated a computational approach, known as Mechanism and Drug Miner (MD-Miner), using a network-based computational approach to predict effective drugs and reveal potential drug mechanisms of action at the level of signaling pathways. Specifically, the patient-specific signaling network is constructed by integrating known disease associated genes with patient-derived gene expression profiles. In parallel, a drug mechanism of action network is constructed by integrating drug targets and z-score profiles of drug-induced gene expression (pre vs. post-drug treatment). Potentially effective candidate drugs are prioritized according to the number of common genes between the patient-specific dysfunctional signaling network and drug MoA network. We evaluated the MD-Miner method on the PC-3 prostate cancer cell line, and showed that it significantly improved the success rate of discovering effective drugs compared with the random selection, and could provide insight into potential mechanisms of action. This work provides a signaling network-based drug repositioning approach. Compared with the reverse gene signature based drug repositioning approaches, the proposed method can provide clues of mechanism of action in terms of signaling transduction networks.

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The data shown below were collected from the profiles of 23 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 55 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 55 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 25%
Researcher 9 16%
Student > Postgraduate 6 11%
Student > Bachelor 3 5%
Professor 3 5%
Other 10 18%
Unknown 10 18%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 12 22%
Agricultural and Biological Sciences 8 15%
Pharmacology, Toxicology and Pharmaceutical Science 5 9%
Medicine and Dentistry 5 9%
Computer Science 5 9%
Other 8 15%
Unknown 12 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 18 May 2018.
All research outputs
#2,964,370
of 25,366,663 outputs
Outputs from BMC Systems Biology
#70
of 1,131 outputs
Outputs of similar age
#52,697
of 329,743 outputs
Outputs of similar age from BMC Systems Biology
#2
of 18 outputs
Altmetric has tracked 25,366,663 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,131 research outputs from this source. They receive a mean Attention Score of 3.7. This one has done particularly well, scoring higher than 93% 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 329,743 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 84% of its contemporaries.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 94% of its contemporaries.