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Multi-target drug repositioning by bipartite block-wise sparse multi-task learning

Overview of attention for article published in BMC Systems Biology, April 2018
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (67th percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

Mentioned by

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8 tweeters
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1 Facebook page

Citations

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3 Dimensions

Readers on

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19 Mendeley
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Title
Multi-target drug repositioning by bipartite block-wise sparse multi-task learning
Published in
BMC Systems Biology, April 2018
DOI 10.1186/s12918-018-0569-7
Pubmed ID
Authors

Limin Li, Xiao He, Karsten Borgwardt

Abstract

Finding potential drug targets is a crucial step in drug discovery and development. Recently, resources such as the Library of Integrated Network-Based Cellular Signatures (LINCS) L1000 database provide gene expression profiles induced by various chemical and genetic perturbations and thereby make it possible to analyze the relationship between compounds and gene targets at a genome-wide scale. Current approaches for comparing the expression profiles are based on pairwise connectivity mapping analysis. However, this method makes the simple assumption that the effect of a drug treatment is similar to knocking down its single target gene. Since many compounds can bind multiple targets, the pairwise mapping ignores the combined effects of multiple targets, and therefore fails to detect many potential targets of the compounds. We propose an algorithm to find sets of gene knock-downs that induce gene expression changes similar to a drug treatment. Assuming that the effects of gene knock-downs are additive, we propose a novel bipartite block-wise sparse multi-task learning model with super-graph structure (BBSS-MTL) for multi-target drug repositioning that overcomes the restrictive assumptions of connectivity mapping analysis. The proposed method BBSS-MTL is more accurate for predicting potential drug targets than the simple pairwise connectivity mapping analysis on five datasets generated from different cancer cell lines. The code can be obtained at http://gr.xjtu.edu.cn/web/liminli/codes .

Twitter Demographics

The data shown below were collected from the profiles of 8 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 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 19 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 21%
Student > Master 3 16%
Student > Doctoral Student 2 11%
Other 2 11%
Student > Bachelor 2 11%
Other 3 16%
Unknown 3 16%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 4 21%
Computer Science 2 11%
Environmental Science 1 5%
Agricultural and Biological Sciences 1 5%
Pharmacology, Toxicology and Pharmaceutical Science 1 5%
Other 5 26%
Unknown 5 26%

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 02 August 2018.
All research outputs
#3,358,650
of 13,322,622 outputs
Outputs from BMC Systems Biology
#202
of 1,077 outputs
Outputs of similar age
#86,461
of 269,251 outputs
Outputs of similar age from BMC Systems Biology
#4
of 16 outputs
Altmetric has tracked 13,322,622 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 1,077 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done well, scoring higher than 80% 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 269,251 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 67% of its contemporaries.
We're also able to compare this research output to 16 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.