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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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  • 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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148 Dimensions

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301 Mendeley
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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 301 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 301 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 53 18%
Researcher 48 16%
Student > Master 18 6%
Student > Bachelor 18 6%
Professor 15 5%
Other 34 11%
Unknown 115 38%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 34 11%
Neuroscience 23 8%
Pharmacology, Toxicology and Pharmaceutical Science 22 7%
Computer Science 19 6%
Medicine and Dentistry 14 5%
Other 63 21%
Unknown 126 42%
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
#120,251
of 25,639,676 outputs
Outputs from Nature Communications
#1,725
of 57,903 outputs
Outputs of similar age
#4,023
of 564,828 outputs
Outputs of similar age from Nature Communications
#73
of 1,966 outputs
Altmetric has tracked 25,639,676 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 57,903 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 55.5. 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 564,828 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 1,966 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.