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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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