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  5. Machine learning enhances prediction of plants as potential sources of antimalarials.

Machine learning enhances prediction of plants as potential sources of antimalarials.

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Resource type
Journal article
Creator (person)
Richard-Bollans, Adam
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Aitken, Conal
Antonelli, Alexandre
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Bitencourt, Cássia
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Goyder, David
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Lucas, Eve
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Ondo, Ian
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Pérez-Escobar, Oscar A.
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Pironon, Samuel
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Richardson, James E.
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Russell, David
Silvestro, Daniele
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Wright, Colin W.
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Howes, Melanie-Jayne R.
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Date published
May 25, 2023
Project(s)
Priority 4: Accelerated Taxonomy
Priority 5: Enhanced Partnerships
Priority 3: Digital Revolution
Priority 2: Trait Diversity and Function
Funder
Funder nameAwards
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Switzerland
PCEFP3_187012
Vetenskapsrådet, Sweden
VR: 2019-04739 - 2019-05191
Stiftelsen för Miljöstrategisk Forskning, Sweden
Research programme BIOPATH (F 2022/1448)
Royal Botanic Gardens, Kew, United Kingdom
Journal title
Frontiers in Plant Science
Volume
14
Article number
1173328
Publisher
Frontiers Research Foundation
Place of publication
Lausanne, Switzerland
eISSN
1664-462X
Date accepted
April 20, 2023
Official URL
https://doi.org/10.3389/fpls.2023.1173328
Rights statement
In Copyright
Licence
https://creativecommons.org/licenses/by/4.0/
DOI
10.3389/fpls.2023.1173328
Keywords
Ethnobotany
Botany
Traditional and indigenous knowledge
Sampling bias
Malaria
Ethnopharmacology
Machine learning
Antiplasmodial activity
Additional information
IF = 6.627 (2022-2023)
Managed by the British Library and supported by the AHRC

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