Application of Artificial Intelligence to the Prediction of the Antimicrobial Activity of Essential Oils
Joint Authors
Daynac, Mathieu
Cortes-Cabrera, Alvaro
Prieto-Garcia, Jose M.
Source
Evidence-Based Complementary and Alternative Medicine
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-09-17
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Essential oils (EOs) are vastly used as natural antibiotics in Complementary and Alternative Medicine (CAM).
Their intrinsic chemical variability and synergisms/antagonisms between its components make difficult to ensure consistent effects through different batches.
Our aim is to evaluate the use of artificial neural networks (ANNs) for the prediction of their antimicrobial activity.
Methods.
The chemical composition and antimicrobial activity of 49 EOs, extracts, and/or fractions was extracted from NCCLS compliant works.
The fast artificial neural networks (FANN) software was used and the output data reflected the antimicrobial activity of these EOs against four common pathogens: Staphylococcus aureus, Escherichia coli, Candida albicans, and Clostridium perfringens as measured by standardised disk diffusion assays.
Results.
ANNs were able to predict >70% of the antimicrobial activities within a 10 mm maximum error range.
Similarly, ANNs were able to predict 2 or 3 different bioactivities at the same time.
The accuracy of the prediction was only limited by the inherent errors of the popular antimicrobial disk susceptibility test and the nature of the pathogens.
Conclusions.
ANNs can be reliable, fast, and cheap tools for the prediction of the antimicrobial activity of EOs thus improving their use in CAM.
American Psychological Association (APA)
Daynac, Mathieu& Cortes-Cabrera, Alvaro& Prieto-Garcia, Jose M.. 2015. Application of Artificial Intelligence to the Prediction of the Antimicrobial Activity of Essential Oils. Evidence-Based Complementary and Alternative Medicine،Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1063331
Modern Language Association (MLA)
Daynac, Mathieu…[et al.]. Application of Artificial Intelligence to the Prediction of the Antimicrobial Activity of Essential Oils. Evidence-Based Complementary and Alternative Medicine No. 2015 (2015), pp.1-9.
https://search.emarefa.net/detail/BIM-1063331
American Medical Association (AMA)
Daynac, Mathieu& Cortes-Cabrera, Alvaro& Prieto-Garcia, Jose M.. Application of Artificial Intelligence to the Prediction of the Antimicrobial Activity of Essential Oils. Evidence-Based Complementary and Alternative Medicine. 2015. Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1063331
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1063331