DocumentCode :
3441557
Title :
Pattern recognition system based on decision trees and fuzzy logic: Anti-HIV molecules application
Author :
Kissi, Mohamed ; Ramdani, Mohammed ; Cherqaoui, Driss
Author_Institution :
Fac. des Sci., Dept. de Math. et Inf., Univ. Chouaib Doukkali, El Jadida, Morocco
fYear :
2009
fDate :
2-4 April 2009
Firstpage :
477
Lastpage :
479
Abstract :
Several works structure activity relationship (SAR) of anti-HIV molecules (Human Immunodeficiency Virus) were studied by different statistical methods and non-linear models (neural networks). But few studies have used the heuristic methods. In this work, we are interested to study this relationship by fuzzy logic and decision trees. The resulting model explain SAR with only tow rules described by three of 7 molecular descriptors. This rules generalize the 79 compounds studied. Decision trees show good performance in the learning and prediction phases.
Keywords :
biology computing; decision trees; fuzzy logic; microorganisms; molecular biophysics; neural nets; pattern recognition; statistical analysis; Human Immunodeficiency Virus; antiHIV molecules application; decision trees; fuzzy logic; neural network; nonlinear model; pattern recognition system; statistical method; structure activity relationship; Artificial neural networks; Chemical compounds; Decision trees; Fuzzy logic; Human immunodeficiency virus; Linear regression; Neural networks; Pattern recognition; Statistical analysis; Testing; decision trees; fuzzy logic; learining; modelin anti-HIV;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Multimedia Computing and Systems, 2009. ICMCS '09. International Conference on
Conference_Location :
Ouarzazate
Print_ISBN :
978-1-4244-3756-6
Electronic_ISBN :
978-1-4244-3757-3
Type :
conf
DOI :
10.1109/MMCS.2009.5256650
Filename :
5256650
Link To Document :
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