Fuzzy Set Theory and Fuzzy Logic and Its Application to Molecular Recognition

Exner, Thomas E.; Keil, Matthias; Brickmann, Jürgen

doi:10.1002/9783527618279.ch39f

Cited by 2 publications

(3 citation statements)

References 75 publications

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“…Fuzzy systems have attracted attention in various fields such as bioinformatics applications, decision-making, and pattern recognition studies and also data analysis [15][16][17][18][19][20][21][22][23][24][25][26]. Fuzzy systems have attracted attention in various fields such as bioinformatics applications, decision-making, and pattern recognition studies and also data analysis [15][16][17][18][19][20][21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

“…Fuzzy logic is a technique that defines and generates responses based on ambiguous and imprecise information. Fuzzy systems have attracted attention in various fields such as bioinformatics applications, decision-making, and pattern recognition studies and also data analysis [15][16][17][18][19][20][21][22][23][24][25][26]. The adaptive neuro-fuzzy inference system (ANFIS) is a fuzzy inference system implemented within the architecture and learning procedure of adaptive networks like a multilayer neural network [17,18].…”

Section: Introductionmentioning

confidence: 99%

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A QSAR study for modeling of thyroid receptors β1 selective ligands by application of adaptive neuro‐fuzzy inference system and radial basis function

Azimi

Afiuni-Zadeh

Karami

2012

Journal of Chemometrics

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A quantitative structure-activity relationship study of thyroid hormone receptors b1 is described in this paper. We used adaptive neuro-fuzzy inference system (ANFIS) and radial basis function (RBF) methods coupling to genetic algorithm (GA) to predict binding affinity of some ligands with b1 thyroid receptors. A set of 83 selective ligands with known affinity of thyroid receptors b1 (pIC 50 ) were selected, and a large number of molecular descriptors were calculated for each molecule by DRAGON. Seven most relevant descriptors were selected by GA-stepwise partial least squares as variable selection tool. The best descriptors (SCBO and EEig08x) and (SCBO, EEig08x, and BEHe1) were applied to train the ANFIS and RBF models, respectively. Then the number and shape of related functions were optimized. The ability and robustness of the GA-ANFIS, GA-RBF, and GA-multiple linear regression (MLR) models in predicting the pIC 50 of thyroid receptors b1 are illustrated by internal validation technique of leave one out and also heuristic and randomized techniques as external validation methods. The results have indicated that the proposed models of ANFIS and RBF in this work are superior to MLR method because of generation of simpler models with only two and three descriptors, respectively. Figure 4. Results of experimental pIC 50 and predicted pIC 50 for the test set of random-3 selection in ANFIS, RBF, and MLR final models.Figure 3. Normality test on residuals of random-3 selection in ANFIS model. QSAR study of TRb1 selective ligands

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A QSAR study for modeling of thyroid receptors β1 selective ligands by application of adaptive neuro‐fuzzy inference system and radial basis function

Azimi

Afiuni-Zadeh

Karami

2012

Journal of Chemometrics

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“…[2][3][4][5][6][7][8] On the other hand, Fuzzy logic is a problem-solving technique that defines and generates responses based on ambiguous, incomplete and imprecise information, and fuzzy systems have fascinated the growing attention and interest in bioinformatics applications, decision-making studies, pattern recognition and data analysis. 9,10 The adaptive neuro-fuzzy inference system (ANFIS) is the achievement of a fuzzy inference system in the form of a multilayer neural network. 11,12 ANFIS can construct an input-output mapping based on a given initial fuzzy system and available input-output data pairs by using a learning procedure.…”

Section: Introductionmentioning

confidence: 99%

A QSAR Study for Modeling of 8-Azaadenine Analogues Proposed as A1 Adenosine Receptor Antagonists Using Genetic Algorithm Coupling Adaptive Neuro-Fuzzy Inference System (ANFIS)

Afiuni-Zadeh

Azimi

2010

ANAL. SCI.

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A quantitative structure activity relationship (QSAR) study of 8-azaadenine, as antagonists for the A1 receptor, is described. A genetic algorithm (GA) method was used as the feature selection tool, and an adaptive neuro-fuzzy inference system (ANFIS) was employed for feature mapping. The best descriptors (GATS4v and BELv7) were applied to train the ANFIS model. The optimum number and shape of related functions were obtained through a subtractive clustering algorithm. The ability and robustness of the GA-ANFIS model in predicting the affinity of 8-azaadenine derivatives (pKi) are illustrated by validation techniques of Leave One Out, heuristic and randomized methods. The results have indicated that the proposed model of ANFIS in this work is superior over two other methods, radial basis function (RBF) and multiple linear regression (MLR).

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Fuzzy Set Theory and Fuzzy Logic and Its Application to Molecular Recognition

Cited by 2 publications

References 75 publications

A QSAR study for modeling of thyroid receptors β1 selective ligands by application of adaptive neuro‐fuzzy inference system and radial basis function

A QSAR study for modeling of thyroid receptors β1 selective ligands by application of adaptive neuro‐fuzzy inference system and radial basis function

A QSAR Study for Modeling of 8-Azaadenine Analogues Proposed as A1 Adenosine Receptor Antagonists Using Genetic Algorithm Coupling Adaptive Neuro-Fuzzy Inference System (ANFIS)

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