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Title: An Assessment of Pharmacological Properties of Schinus Essential Oils: A Soft Computing Approach
Authors: Neves, José
Martins, M. Rosário
Candeias, Fátima
Arantes, Silvia
Piteira, Ana
Vicente, Henrique
Editors: Claus, Thorsten
Herrmann, Frank
Manitz, Michael
Rose, Oliver
Keywords: Schinus spp.
Essential Oils
Logic Programming
Case Base Reasoning
Knowledge Representation and Reasoning
Similarity Analysis
Issue Date: 2016
Publisher: European Council for Modelling and Simulation
Citation: Neves, J., Martins, M.R., Candeias, F., Arantes, S., Piteira, A. & Vicente, H., An Assessment of Pharmacological Properties of Schinus Essential Oils – A Soft Computing Approach. In T. Claus, F. Herrmann, M. Manitz & O. Rose Eds., Proceedings 30th European Conference on Modelling and Simulation (ECMS 2016), pp. 107–113, European Council for Modelling and Simulation Edition, 2016.
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 self-contradictory 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.
ISBN: 978-0-9932440-2-5
Type: article
Appears in Collections:QUI - Artigos em Livros de Actas/Proceedings
HERCULES - Artigos em Livros de Actas/Proceedings

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