High‐dimensional QSAR classification model for anti‐hepatitis C virus activity of thiourea derivatives based on the sparse logistic regression model with a bridge penalty

Algamal, Zakariya Yahya; Lee, Muhammad Hisyam; Al-Fakih, Abdo Mohammed; Aziz, Madzlan

doi:10.1002/cem.2889

Cited by 30 publications

(9 citation statements)

References 45 publications

(56 reference statements)

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“…Unfortunately, these common issues are not limited only to QSARs for prediction of antioxidant activity, but also other biological activities, as well as prediction of properties such as chromatographic retention time …”

Section: Introductionmentioning

confidence: 99%

Interpretation of ANN‐based QSAR models for prediction of antioxidant activity of flavonoids

2018

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Quantitative structure-activity relationships (QSARs) built using machine learning methods, such as artificial neural networks (ANNs) are powerful in prediction of (antioxidant) activity from quantum mechanical (QM) parameters describing the molecular structure, but are usually not interpretable. This obvious difficulty is one of the most common obstacles in application of ANN-based QSAR models for design of potent antioxidants or elucidating the underlying mechanism. Interpreting the resulting models is often omitted or performed erroneously altogether. In this work, a comprehensive comparative study of six methods (PaD, PaD , weights, stepwise, perturbation and profile) for exploration and interpretation of ANN models built for prediction of Trolox-equivalent antioxidant capacity (TEAC) QM descriptors, is presented. Sum of ranking differences (SRD) was used for ranking of the six methods with respect to the contributions of the calculated QM molecular descriptors toward TEAC. The results show that the PaD, PaD and profile methods are the most stable and give rise to realistic interpretation of the observed correlations. Therefore, they are safely applicable for future interpretations without the opinion of an experienced chemist or bio-analyst. © 2018 Wiley Periodicals, Inc.

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Section: Introductionmentioning

confidence: 99%

Interpretation of ANN‐based QSAR models for prediction of antioxidant activity of flavonoids

2018

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“…Aside from that, overfitting occurs in high-dimensional datasets, such as gene data sets, because the number of features (genes) is greater than the number of observations. In addition, the estimates produced by it may be negatively affected by multicollinearity [15].…”

Section: Regularized Logistic Regressionmentioning

confidence: 99%

Improving the Diagnosis of Breast Cancer Using Regularized Logistic Regression with Adaptive Elastic Net

Alharthi¹,

Lee²,

Algama³

2021

ujph

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“…According to the IC 50 (half maximal inhibitory concentration) values, the compounds are divided into two classes: An inhibitor class (0.008 μM <IC 50 <0.3 μM) containing 38 compounds and a non-inhibitor class (IC 50 >0.3 μM) containing 37 compounds (Cao et al, 2015). A dataset of 121 molecules of thiourea derivatives with anti-HCV activity and 2559 descriptors was used by Algamal et al (2017). According to their experimental EC 50 (the concentration of a drug that gives half-maximal response), the molecules were divided into two categories by the threshold value of 0.1 μM: actives (EC 50 <0.1 μM) and inactives (EC 50 >0.1 μM).…”

Section: J Eng Applied Sci 15 (1): 310-318 2020mentioning

confidence: 99%

Selection of Tuning Parameter in L1-Support Vector Machine via. Particle Swarm Optimization Method

Al-Thanoon¹,

Qasim²,

Algamal³

2019

J. Eng. Appl. Sci.

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Descriptor selection for classification methods is one of the most important topics in the chemometrics. The selection of descriptors can be considered to be a variable selection problem that aims to find a small subset of descriptors that has the most discriminative information for the classification target. Penalized Support Vector Machine (PSVM) is one of the most effective embedded methods and it is more preferable than the Support Vector Machine (SVM) because PSVM combines the standard SVM with a penalty to simultaneously perform both variable selection and classification. The PSVM with L1-norm is the most widely used methods. However, the efficiency of PSVM with L1-norm depends on appropriately choosing the tuning parameter which is involved in the L1-norm penalty. In this study, a particle swarm optimization method which is a metaheuristic continuous algorithm is proposed to determine the tuning parameter in PSVM with L1-norm penalty. The proposed method will efficiently help to find the most significant descriptors in constructing Quantitative Structure-Activity Relationship classification (QSAR) model with high classification performance. Depend on the four datasets, the experimental results show the favorable performance of the proposed method when the number of descriptors is high and the sample size is low comparing with other competitor methods.

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High‐dimensional QSAR classification model for anti‐hepatitis C virus activity of thiourea derivatives based on the sparse logistic regression model with a bridge penalty

Cited by 30 publications

References 45 publications

Interpretation of ANN‐based QSAR models for prediction of antioxidant activity of flavonoids

Interpretation of ANN‐based QSAR models for prediction of antioxidant activity of flavonoids

Improving the Diagnosis of Breast Cancer Using Regularized Logistic Regression with Adaptive Elastic Net

Selection of Tuning Parameter in L1-Support Vector Machine via. Particle Swarm Optimization Method

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