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Machine learning for predicting lifespan-extending chemical compounds

Abstract:

Increasing age is a risk factor for many diseases; therefore developing pharmacological interventions that slow down ageing and consequently postpone the onset of many age-related diseases is highly desirable. In this work we analyse data from the DrugAge database, which contains chemical compounds and their effect on the lifespan of model organisms. Predictive models were built using the machine learning method random forests to predict whether or not a chemical compound will increase Caenor...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Publisher's version

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Publisher copy:
10.18632/aging.101264

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Department:
Oxford, MSD, Psychiatry
Role:
Author
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Publisher:
Impact Journals Publisher's website
Journal:
Aging Journal website
Volume:
9
Issue:
7
Pages:
1721—1737
Publication date:
2017-07-05
Acceptance date:
2017-07-12
DOI:
EISSN:
1945-4589
Pubs id:
pubs:708540
URN:
uri:2a997f0b-1d0a-4284-9213-36b185291952
UUID:
uuid:2a997f0b-1d0a-4284-9213-36b185291952
Local pid:
pubs:708540

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