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Machine learning identifies candidates for drug repurposing in Alzheimer’s disease

Overview of attention for article published in Nature Communications, February 2021
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Citations

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142 Dimensions

Readers on

mendeley
299 Mendeley
Title
Machine learning identifies candidates for drug repurposing in Alzheimer’s disease
Published in
Nature Communications, February 2021
DOI 10.1038/s41467-021-21330-0
Pubmed ID
Authors

Steve Rodriguez, Clemens Hug, Petar Todorov, Nienke Moret, Sarah A. Boswell, Kyle Evans, George Zhou, Nathan T. Johnson, Bradley T. Hyman, Peter K. Sorger, Mark W. Albers, Artem Sokolov

Abstract

Clinical trials of novel therapeutics for Alzheimer's Disease (AD) have consumed a large amount of time and resources with largely negative results. Repurposing drugs already approved by the Food and Drug Administration (FDA) for another indication is a more rapid and less expensive option. We present DRIAD (Drug Repurposing In AD), a machine learning framework that quantifies potential associations between the pathology of AD severity (the Braak stage) and molecular mechanisms as encoded in lists of gene names. DRIAD is applied to lists of genes arising from perturbations in differentiated human neural cell cultures by 80 FDA-approved and clinically tested drugs, producing a ranked list of possible repurposing candidates. Top-scoring drugs are inspected for common trends among their targets. We propose that the DRIAD method can be used to nominate drugs that, after additional validation and identification of relevant pharmacodynamic biomarker(s), could be readily evaluated in a clinical trial.

X Demographics

X Demographics

The data shown below were collected from the profiles of 94 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 299 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 299 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 52 17%
Researcher 49 16%
Student > Master 18 6%
Student > Bachelor 18 6%
Professor 14 5%
Other 41 14%
Unknown 107 36%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 35 12%
Neuroscience 23 8%
Pharmacology, Toxicology and Pharmaceutical Science 22 7%
Computer Science 18 6%
Medicine and Dentistry 14 5%
Other 70 23%
Unknown 117 39%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 294. 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 07 June 2023.
All research outputs
#118,456
of 25,369,304 outputs
Outputs from Nature Communications
#1,693
of 56,714 outputs
Outputs of similar age
#3,952
of 562,007 outputs
Outputs of similar age from Nature Communications
#74
of 1,967 outputs
Altmetric has tracked 25,369,304 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 56,714 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 55.7. This one has done particularly well, scoring higher than 97% 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 562,007 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 1,967 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 96% of its contemporaries.