Application of genetic algorithms for pixel selection in multivariate image analysis for a QSAR study of trypanocidal activity for quinone compounds and design new quinone compounds

Sarkhosh, Maryam; Khorshidi, Neda; Ниази, Али; Leardi, Riccardo

doi:10.1016/j.chemolab.2014.09.004

Cited by 10 publications

(7 citation statements)

References 39 publications

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“…Genetic algorithm (GA), as a popular heuristic optimization technique, uses a probabilistic, nonlocal variable search process inspired by Darwin's theory of natural selection . Genetic algorithm has the advantage of exploring the space of all possible subsets fairly well in a large but reasonable amount of time, which is much less than that required for the study of all possible subsets . Many variants of GA have been developed for combinatorial optimization, and numerous references have already shown that GA can be successfully used as a spectral variable selection technique …”

Section: Introductionmentioning

confidence: 99%

“…17,18 Genetic algorithm has the advantage of exploring the space of all possible subsets fairly well in a large but reasonable amount of time, which is much less than that required for the study of all possible subsets. [19][20][21] Many variants of GA have been developed for combinatorial optimization, [22][23][24][25][26] and numerous references have already shown that GA can be successfully used as a spectral variable selection technique. [27][28][29][30] A major problem with GA is the stochastic risk, since the probability of finding a suitable model is only by chance (ie, due to random correlations).…”

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confidence: 99%

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Similar offspring voting genetic algorithm for spectral variable selection

Zheng

2017

Journal of Chemometrics

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This paper proposes a novel spectral variable selection approach named similar offspring voting genetic algorithm (sovGA). Unlike the traditional voting method (here called voting genetic algorithm) that attempts to overcome the stochastic risk of a genetic algorithm but may damage its optimal variable combination, sovGA aims to achieve a trade‐off between prediction precision and reliability of models. Genetic algorithm was repeated many times, and some effective similar offspring were obtained by a proposed algorithm; then, the spectral variables were selected by a voting mechanism. Two calibration methods, partial least square regression and multiple linear regression, were constructed to evaluate performance. The experimental results show that sovGA outperformed genetic algorithm, voting genetic algorithm, and the full spectrum situation in most cases.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

Similar offspring voting genetic algorithm for spectral variable selection

Zheng

2017

Journal of Chemometrics

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“…Recently, image analysis has been considered as a low cost and fast method to extract appropriate descriptors to develop QSAR models. Despite of 3D descriptors, they do not need molecular and quantum optimization by commercial software such as Hyperchem and Gaussian . Different applications of image analysis was reviewed by Prats‐Montalbán et al .…”

Section: Introductionmentioning

confidence: 99%

Image‐Based Analysis to Predict the Activity of Tariquidar Analogs as P‐Glycoprotein Inhibitors: The Importance of External Validation

Shayanfar

Ghandadi

2015

Archiv der Pharmazie

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Permeability glycoprotein (P-gp) is involved in the pathology of various diseases including cancer and epilepsy, mainly through the translocation of some medicines across the cell membrane. Here, we employed image-based quantitative structure-activity relationship (QSAR) models to predict the P-gp inhibitory activity of some Tariquidar derivatives. The structures of 65 Tariquidar derivatives and their P-gp inhibition activities were collected from the literature. For each compound, the pixels of bidimensional images and their principal components (PCs) were calculated using MATLAB software. Various statistical methods including principal component regression, artificial neural networks, and support vector machines were employed to investigate the correlation between the PCs and the activity of the compounds. The predictability of the models was investigated using external validation and applicability domain analysis. An artificial neural network-based model demonstrated the best prediction results for the test set. Moreover, external validation analysis of the developed models supports the idea that R(2) cannot assure the validity of QSAR models and another criterion, i.e., the concordance correlation coefficient (CCC) parameter, should be involved to evaluate the validity of the QSAR models. The results of this study indicate that image analysis could be as suitable as descriptors calculated by commercial software to predict the activity of drug-like molecules.

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“…Most works in quantitative chemical analysis have used Genetic Algorihms (GAs) as a reference to select variables (Niazi and Leardi, 2012;Ferrand et al, 2011;Cong et al, 2013;Yun et al, 2014;Sarkhosh et al, 2014;Wang et al 2015). As reviewed by Niazi and Leardi (2012), in the last decades GAs have been even more frequently used to solve different kinds of problems in chemistry data.…”

Section: Introductionmentioning

confidence: 99%

“…Based on their results, the authors showed that GA-PLS was able to perform an improvement on variable selection compared to the original GA-PLS. Finally, Sarkhosh et al (2014) proposed an application of GAs for pixel selection in multivariate image analysis for a QSAR study of trypanocidal activity for quinone compounds and design new quinone compounds. They investigated the pixel selection effect by genetic algorithm application for PLS model.…”

Section: Introductionmentioning

confidence: 99%

Modern Metaheuristic with Multi-Objective Formulation for the Variable Selection Problem

Paula

Soares

et al. 2017

Journal of Computer Science

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The development of efficient algorithms for variable selection becomes important to deal with large and complex datasets. Most works in quantitative chemical analysis have used Genetic Algorithms (GAs) as a reference method to select variables. On the other hand, new advances in metaheuristic techniques provide novel possibilities in this task Moreover, the application of Multi-Objective Optimization (MOO) may significantly contribute to efficiently construct an accurate model in the context of multivariate calibration. MOO has showed itself as an efficiently and successful tool to dealing with conflicting objective-functions. For instance, the use of MOO may be considered as a good choice to treat the reducing of prediction error and the number of selected variables in a calibration model. In this paper, we present a modern metaheuristic implementation called Multi-Objective Firefly Algorithm (MOFA) for variable selection in multivariate calibration models. The goal is to propose an optimization to reduce the prediction error of the property of interest in the analysed sample as well as reducing the number of selected variables. However, the outcomes are remarkably promising compared with the previous work. Based on the results obtained, it is possible to demonstrate that our proposal is a viable alternative in order to deal with such conflicting objectives. Additionally, we compare MOFA with a traditional GA implementation and show that MOFA is more efficient for the variable selection problem.

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Application of genetic algorithms for pixel selection in multivariate image analysis for a QSAR study of trypanocidal activity for quinone compounds and design new quinone compounds

Cited by 10 publications

References 39 publications

Similar offspring voting genetic algorithm for spectral variable selection

Similar offspring voting genetic algorithm for spectral variable selection

Image‐Based Analysis to Predict the Activity of Tariquidar Analogs as P‐Glycoprotein Inhibitors: The Importance of External Validation

Modern Metaheuristic with Multi-Objective Formulation for the Variable Selection Problem

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