- Abstract:
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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...
Expand abstract - Publication status:
- Published
- Peer review status:
- Peer reviewed
- Version:
- Publisher's version
- Files:
-
-
(pdf, 378.9KB)
-
- Publisher copy:
- 10.18632/aging.101264
-
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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:
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pubs:708540
- URN:
-
uri:2a997f0b-1d0a-4284-9213-36b185291952
- UUID:
-
uuid:2a997f0b-1d0a-4284-9213-36b185291952
- Local pid:
- pubs:708540
- Copyright holder:
- Barardo et al
- Copyright date:
- 2017
- Notes:
- Barardo et al. This is an open‐access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Journal article
Machine learning for predicting lifespan-extending chemical compounds
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