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Cogena, a novel tool for co-expressed gene-set enrichment analysis, applied to drug repositioning and drug mode of action discovery

Overview of attention for article published in BMC Genomics, May 2016
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  • Good Attention Score compared to outputs of the same age (70th percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

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8 X users

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Title
Cogena, a novel tool for co-expressed gene-set enrichment analysis, applied to drug repositioning and drug mode of action discovery
Published in
BMC Genomics, May 2016
DOI 10.1186/s12864-016-2737-8
Pubmed ID
Authors

Zhilong Jia, Ying Liu, Naiyang Guan, Xiaochen Bo, Zhigang Luo, Michael R. Barnes

Abstract

Drug repositioning, finding new indications for existing drugs, has gained much recent attention as a potentially efficient and economical strategy for accelerating new therapies into the clinic. Although improvement in the sensitivity of computational drug repositioning methods has identified numerous credible repositioning opportunities, few have been progressed. Arguably the "black box" nature of drug action in a new indication is one of the main blocks to progression, highlighting the need for methods that inform on the broader target mechanism in the disease context. We demonstrate that the analysis of co-expressed genes may be a critical first step towards illumination of both disease pathology and mode of drug action. We achieve this using a novel framework, co-expressed gene-set enrichment analysis (cogena) for co-expression analysis of gene expression signatures and gene set enrichment analysis of co-expressed genes. The cogena framework enables simultaneous, pathway driven, disease and drug repositioning analysis. Cogena can be used to illuminate coordinated changes within disease transcriptomes and identify drugs acting mechanistically within this framework. We illustrate this using a psoriatic skin transcriptome, as an exemplar, and recover two widely used Psoriasis drugs (Methotrexate and Ciclosporin) with distinct modes of action. Cogena out-performs the results of Connectivity Map and NFFinder webservers in similar disease transcriptome analyses. Furthermore, we investigated the literature support for the other top-ranked compounds to treat psoriasis and showed how the outputs of cogena analysis can contribute new insight to support the progression of drugs into the clinic. We have made cogena freely available within Bioconductor or https://github.com/zhilongjia/cogena . In conclusion, by targeting co-expressed genes within disease transcriptomes, cogena offers novel biological insight, which can be effectively harnessed for drug discovery and repositioning, allowing the grouping and prioritisation of drug repositioning candidates on the basis of putative mode of action.

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X Demographics

The data shown below were collected from the profiles of 8 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 98 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Denmark 1 1%
Unknown 97 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 20 20%
Student > Ph. D. Student 18 18%
Student > Bachelor 12 12%
Other 6 6%
Student > Postgraduate 6 6%
Other 20 20%
Unknown 16 16%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 21 21%
Agricultural and Biological Sciences 19 19%
Medicine and Dentistry 13 13%
Computer Science 9 9%
Engineering 4 4%
Other 12 12%
Unknown 20 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 June 2016.
All research outputs
#6,344,569
of 23,577,761 outputs
Outputs from BMC Genomics
#2,658
of 10,800 outputs
Outputs of similar age
#99,450
of 340,335 outputs
Outputs of similar age from BMC Genomics
#49
of 196 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 10,800 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done well, scoring higher than 75% 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 340,335 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 70% of its contemporaries.
We're also able to compare this research output to 196 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.