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

Authors
  • Rodriguez, Steve1, 2
  • Hug, Clemens1
  • Todorov, Petar1
  • Moret, Nienke1
  • Boswell, Sarah A.1
  • Evans, Kyle1, 2
  • Zhou, George1, 2
  • Johnson, Nathan T.1
  • Hyman, Bradley T.2
  • Sorger, Peter K.1
  • Albers, Mark W.1, 2
  • Sokolov, Artem1
  • 1 Harvard Medical School,
  • 2 Massachusetts General Hospital,
Type
Published Article
Journal
Nature Communications
Publisher
Springer Nature
Publication Date
Feb 15, 2021
Volume
12
Identifiers
DOI: 10.1038/s41467-021-21330-0
PMID: 33589615
PMCID: PMC7884393
Source
PubMed Central
Keywords
License
Unknown

Abstract

Clinical trials of novel therapeutics for Alzheimer’s Disease (AD) have provided largely negative results, so far. Here, the authors present a machine learning framework that quantifies potential associations between the pathology of AD severity and gene-based molecular mechanisms to enable drug repurposing.

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