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Labjar, Houda, Al-Sarem, Mohammad, Kissi, Mohamed. (1400). Feature Selection Using a Genetic Algorithms and Fuzzy logic in Anti-Human Immunodeficiency Virus Prediction for Drug Discovery. , 14(Special Issue: 5th International Conference of Reliable Information and Communication Technology (IRICT 2020)), 23-36. doi: 10.22059/jitm.2022.84883
Houda Labjar; Mohammad Al-Sarem; Mohamed Kissi. "Feature Selection Using a Genetic Algorithms and Fuzzy logic in Anti-Human Immunodeficiency Virus Prediction for Drug Discovery". , 14, Special Issue: 5th International Conference of Reliable Information and Communication Technology (IRICT 2020), 1400, 23-36. doi: 10.22059/jitm.2022.84883
Labjar, Houda, Al-Sarem, Mohammad, Kissi, Mohamed. (1400). 'Feature Selection Using a Genetic Algorithms and Fuzzy logic in Anti-Human Immunodeficiency Virus Prediction for Drug Discovery', , 14(Special Issue: 5th International Conference of Reliable Information and Communication Technology (IRICT 2020)), pp. 23-36. doi: 10.22059/jitm.2022.84883
Labjar, Houda, Al-Sarem, Mohammad, Kissi, Mohamed. Feature Selection Using a Genetic Algorithms and Fuzzy logic in Anti-Human Immunodeficiency Virus Prediction for Drug Discovery. , 1400; 14(Special Issue: 5th International Conference of Reliable Information and Communication Technology (IRICT 2020)): 23-36. doi: 10.22059/jitm.2022.84883

Feature Selection Using a Genetic Algorithms and Fuzzy logic in Anti-Human Immunodeficiency Virus Prediction for Drug Discovery

Journal of Information Technology Management
دوره 14، Special Issue: 5th International Conference of Reliable Information and Communication Technology (IRICT 2020)، 2022، صفحه 23-36    اصل مقاله (671.84 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/jitm.2022.84883
نویسندگان
Houda Labjar1؛ Mohammad Al-Sarem2؛ Mohamed Kissi* 3
1Researcher, Laboratory Processes and Environment, Faculty of Sciences and Technology, University Hassan II Casablanca, Mohammedia, Morocco.
2Associate Professor, Information System Departement, Taibah University, Al-Madinah Al-Monawarah, Saudi Arabia.
3Full Professor, LIM Laboratory, Computer Science Department, Faculty of Sciences and Technology, University Hassan II Casablanca, Mohammedia, Morocco.
چکیده
This paper presents an approach that uses both genetic algorithm (GA) and fuzzy inference system (FIS), for feature selection for descriptor in a quantitative structure activity relationships (QSAR) classification and prediction problem. Unlike the traditional techniques that employed GA, the FIS is used to evaluate an individual population in the GA process. So, the fitness function is introduced and defined by the error rate of the GA and FIS combination. The proposed approach has been implemented and tested using a data set with experimental value anti-human immunodeficiency virus (HIV) molecules. The statistical parameters q2 (leave many out) is equal 0.59 and r (coefficient of correlation) is equal 0.98. These results reveal the capacity for achieving subset of descriptors, with high predictive capacity as well as the effectiveness and robustness of the proposed approach.
کلیدواژه‌ها
Feature Selection؛ Machine Learning؛ Computational Chemistry؛ QSAR؛ Fuzzy Logic؛ Genetic Algorithms
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