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SAR and QSAR in Environmental Research Volume 31, 2020 - Issue 8
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Research Article

Quantitative structure-activity relationship model for classifying the diverse series of antifungal agents using ratio weighted penalized logistic regression

A.M. Alharthia Department of Mathematical Sciences, Universiti Teknologi Malaysia, Skudai, Malaysia
,
M.H. Leea Department of Mathematical Sciences, Universiti Teknologi Malaysia, Skudai, MalaysiaCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0002-3700-2363
ORCID Icon,
Z.Y. Algamalb Department of Statistics and Informatics, University of Mosul, Mosul, IraqORCID Iconhttps://orcid.org/0000-0002-0229-7958
ORCID Icon &
A.M. Al-Fakihc Department of Chemistry, Faculty of Science, Universiti Teknologi Malaysia, Johor, Malaysia
Pages 571-583 | Received 14 Apr 2020, Accepted 10 Jun 2020, Published online: 06 Jul 2020

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A.M. Al-Fakih, M.K. Qasim, Z.Y. Algamal, A.M. Alharthi & M.H. Zainal-Abidin. (2023) QSAR classification model for diverse series of antifungal agents based on binary coyote optimization algorithm. SAR and QSAR in Environmental Research 34:4, pages 285-298.
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L.A. Stolbov, D.A. Filimonov & V.V. Poroikov. (2022) SAR based on self consistent classifier. SAR and QSAR in Environmental Research 33:10, pages 793-804.
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Z. Mozafari, M. Arab Chamjangali, M. Arashi & N. Goudarzi. (2021) Suggestion of active 3-chymotrypsin like protease (3CLPro) inhibitors as potential anti-SARS-CoV-2 agents using predictive QSAR model based on the combination of ALASSO with an ANN model. SAR and QSAR in Environmental Research 32:11, pages 863-888.
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Articles from other publishers (3)

Zeinab Mozafari, Mansour Arab Chamjangali, Mohammad Arashi & Nasser Goudarzi. (2022) Application of the LAD-LASSO as a dimensional reduction technique in the ANN-based QSAR study: Discovery of potent inhibitors using molecular docking simulation. Chemometrics and Intelligent Laboratory Systems 222, pages 104510.
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Oscar Jossa Bastidas, Begonya Garcia-Zapirain, Alberto Loizate Totoricaguena, Sofia Zahia & Jon Uriarte Carpio. (2021) Feature analysis and prediction of complications in ostomy patients based on laboratory analytical data using a machine learning approach. Feature analysis and prediction of complications in ostomy patients based on laboratory analytical data using a machine learning approach.
Hiromasa Kaneko. (2021) Examining variable selection methods for the predictive performance of regression models and the proportion of selected variables and selected random variables. Heliyon 7:6, pages e07356.
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