|
Digital
Library of the European Council for Modelling and Simulation |
Title: |
An
Assessment Of Pharmacological Properties Of Schinus
Essential Oils - A Soft Computing Approach |
Authors: |
Jose Neves,
M. Rosario Martins, Fatima Candeias, Silvia Arantes, Ana Piteira, Henrique Vicente |
Published in: |
(2016).ECMS 2016 Proceedings edited
by: Thorsen Claus, Frank Herrmann, Michael Manitz, Oliver Rose, European Council for Modeling and
Simulation. doi:10.7148/2016 ISBN:
978-0-9932440-2-5 30th
European Conference on Modelling and Simulation, Regensburg Germany, May 31st
– June 3rd, 2016 |
Citation
format: |
Jose Neves,
M. Rosario Martins, Fatima Candeias, Silvia Arantes, Ana Piteira, Henrique
Vicente (2016). An Assessment Of Pharmacological Properties Of Schinus Essential Oils - A Soft Computing Approach, ECMS
2016 Proceedings edited by: Thorsten Claus, Frank Herrmann, Michael Manitz, Oliver Rose European
Council for Modeling and Simulation. doi:10.7148/2016-0107 |
DOI: |
http://dx.doi.org/10.7148/2016-0107 |
Abstract: |
Plants of genus Schinus
are native South America and introduced in Mediterranean countries, a long
time ago. Some Schinus species have been used in
folk medicine, and Essential Oils of Schinus spp. (EOs) have been reported as having antimicrobial, anti-tumoural and anti-inflammatory properties. Such assets
are related with the EOs chemical composition that
depends largely on the species, the geographic and climatic region, and on
the part of the plants used. Considering the difficulty to infer the
pharmacological properties of EOs of Schinus species without a hard experimental setting, this
work will focus on the development of an Artificial Intelligence grounded
Decision Support System to predict pharmacological properties of Schinus EOs. The computational
framework was built on top of a Logic Programming Case Base approach to
knowledge representation and reasoning, which caters to the handling of
incomplete, unknown, or even selfcontradictory information.
New clustering methods centered on an analysis of attribute’s similarities
were used to distinguish and aggregate historical data according to the
context under which it was added to the Case Base, therefore enhancing the
prediction process. |
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