Prediction of electrophoretic mobilities of sulfonamides in capillary zone electrophoresis using artificial neural networks

Jalali‐Heravi, Mehdi; Garkani-Nejad, Zahra

doi:10.1016/s0021-9673(01)01099-8

Cited by 62 publications

(33 citation statements)

References 18 publications

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“…Also, the network parameters of the number of neurons at the hidden layer, learning rate, and momentum were optimized. Procedures for optimization of these parameters were reported in previous papers [27][28][29][30].…”

Section: Neural Network Generationmentioning

confidence: 99%

“…According to the neural network methodology, the number of hidden nodes and iterations and the learning and momentum terms were optimized in the present work. The values of these parameters were optimized with the procedure that was reported in our previous works [27][28][29][30]. The hidden layer could be increased to a large number of nodes without the BP algorithm being able to reduce the errors.…”

Section: Ann Analysismentioning

confidence: 99%

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Prediction of electrophoretic mobilities of peptides in capillary zone electrophoresis by quantitative structure-mobility relationships using the offord model and artificial neural networks

et al. 2005

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The aim of this work was to explore the usefulness of empirical models and multivariate analysis techniques in predicting electrophoretic mobilities of small peptides in capillary zone electrophoresis (CZE). The data set consists of electrophoretic mobilities, measured at pH 2.5, for 125 peptides ranging in size between 2 and 14 amino acids. Among the existing empirical models, the Offord model (i.e., mu identical with Q/M(2/3)) gave the best correlation for the data set. A quantitative structure-mobility relationship (QSMR) was developed using the Offord's charge-over-mass term (Q/M(2/3)) as one descriptor combined with the corrected steric substituent constant (E(s, c)) and molar refractivity (MR) descriptors to account for the steric effects and bulkiness of the amino acid side chains. The multilinear regression (MLR) of the data set showed an improvement in the predictive ability of the model over the simple Offord's relationship. A 3-4-1 back propagation artificial neural networks (BP-ANN) model resulted in a significant improvement in the predictive ability of the QSMR over the MLR treatment, especially for peptides of higher charges that contain basic amino acids arginine, histidine, and lysine. The improved correlations by the BP-ANN analysis suggest the existence of nonlinear characteristic in the mobility-charge relationships.

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Section: Neural Network Generationmentioning

confidence: 99%

Section: Ann Analysismentioning

confidence: 99%

Prediction of electrophoretic mobilities of peptides in capillary zone electrophoresis by quantitative structure-mobility relationships using the offord model and artificial neural networks

et al. 2005

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“…These properties of ANN provide higher flexibility and capability in data fitting, prediction, and modeling of non-linear relationships. A detailed description of the theory behind a neural network has been adequately described elsewhere [37][38][39][40]. The structure of the network comprised of three node layers: an input, a hidden and an output layer.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Application of gold-labeled antibody biosensor in simultaneous determination of total aflatoxins using artificial neural network

Saidi

Mirzaei

2013

J IRAN CHEM SOC

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Preparation of antibody-coated gold nanoparticles (GNPs) specific to aflatoxins B1, B2, G1 and G2 and its use in developing aflatoxins diagnostic method were presented in this paper. The formation of gold-labeled antibodies was accomplished at optimal condition. Due to severe overlapping between the emission profiles for the aflatoxins, they cannot be determined by direct inspection of data. The strategy used in this study, constituted by artificial neural network (ANN), was easy to implement and to originate reliable results. ANN can be successfully applied to spectrofluorimetric spectra matrices to simultaneous determination of total aflatoxins. Quantitative results obtained using ANN method for aflatoxins in pistachio nuts samples were compared to those obtained using the HPLC method. Obtained results using these two methods did not show significant differences.

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“…One approach is to use a mathematical equation to correlate electrophoretic mobility with the molecular parameters [2][3][4][5][6][7]. The other methods are more empirically based on quantitative structure-property relationship (QSPR) approaches using techniques such as multiple linear regression, artificial neural network [8][9][10][11][12][13][14][15], and support vector machine [16].…”

Section: Introductionmentioning

confidence: 99%

Development of migration models for acids in capillary electrophoresis using heuristic and radial basis function neural network methods

et al. 2005

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A quantitative structure-mobility relationship (QSMR) was developed for the absolute mobilities of a diverse set of 277 organic and inorganic acids in capillary electrophoresis based on the descriptors calculated from the structure alone. The heuristic method (HM) and the radial basis function neural networks (RBFNN) were utilized to construct the linear and nonlinear prediction models, respectively. The prediction results were in agreement with the experimental values. The HM model gave a root-mean-square (RMS) error of 3.66 electrophoretic mobility units for the training set, 4.67 for the test set, and 3.88 for the whole data set, while the RBFNN gave an RMS error of 2.49, 3.19, and 2.65, respectively. The heuristic linear model could give some insights into the factors that are likely to govern the mobilities of the compounds, however, the prediction results of the RBFNN model seem to be better than that of the HM.

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Prediction of electrophoretic mobilities of sulfonamides in capillary zone electrophoresis using artificial neural networks

Cited by 62 publications

References 18 publications

Prediction of electrophoretic mobilities of peptides in capillary zone electrophoresis by quantitative structure-mobility relationships using the offord model and artificial neural networks

Prediction of electrophoretic mobilities of peptides in capillary zone electrophoresis by quantitative structure-mobility relationships using the offord model and artificial neural networks

Application of gold-labeled antibody biosensor in simultaneous determination of total aflatoxins using artificial neural network

Development of migration models for acids in capillary electrophoresis using heuristic and radial basis function neural network methods

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