Feature Selection Using a Genetic Algorithms and Fuzzy logic in Anti-Human Immunodeficiency Virus Prediction for Drug Discovery
| Autor: | Houda Labjar, Mohammad Al-Sarem, Mohamed Kissi |
|---|---|
| Jazyk: | perština |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Journal of Information Technology Management, Vol 14, Iss 5th International Conference of Reliable Information and Communication Technology (IRICT 2020), Pp 23-36 (2022) |
| Druh dokumentu: | article |
| ISSN: | 2008-5893 2423-5059 |
| DOI: | 10.22059/jitm.2022.84883 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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