Classification of sphingosine kinase inhibitors using counter propagation artificial neural networks: A systematic route for designing selective SphK inhibitors

Neiband, Marzieh Sadat; Mani-Varnosfaderani, Ahmad; Benvidi, Ali

doi:10.1080/1062936x.2017.1280535

Cited by 5 publications

(3 citation statements)

References 52 publications

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“…The collected FT‐IR data were mean centered and then autoscaled according to the standard deviation of the wavenumbers. For this standardization, the FT‐IR information (D 1 ) was transformed into the D 2 value according to the following equation (Neiband et al., 2017):

D_{2} = \frac{D_{1} ‐ D_{m}}{δ_{s}}

…”

Section: Methodsmentioning

confidence: 99%

“…The CPANN method is able to solve the nonlinear mapping problems which PCA and LDA are not able to solve (Melssen et al., 2006). The CPANN architecture has two layers of neurons: a Kohonen layer and an output layer (Grosberg layer) that performs the mapping of the multidimensional input data into the lower dimensional array (two dimensions) (Neiband et al., 2017). The Kohonen layer is composed of a grid of N 2 neurons (where N is the number of neurons).…”

Section: Introductionmentioning

confidence: 99%

“…During training, all samples are placed in the mapping according to the assigned classes, and the number of the weights of the neurons is the same as the number of the variables in the original data. More detailed about the theory of CPANN algorithm can be found in literature (Neiband et al., 2017).…”

Section: Introductionmentioning

confidence: 99%

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Detection of fraud in lime juice using pattern recognition techniques and FT‐IR spectroscopy

Mohammadian

Barzegar

Mani-Varnosfaderani

2021

Food Science & Nutrition

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The lime juice is one of the products that has always fallen victim to fraud by manufacturers for reducing the cost of products. The aim of this research was to determine fraud in distributed lime juice products from different factories in Iran. In this study, 101 samples were collected from markets and also prepared manually and finally derived into 5 classes as follows: two natural classes (Citrus limetta, Citrus aurantifolia), including 17 samples, and three reconstructed classes, including 84 samples (made from Spanish concentrate, Chinese concentrate, and concentrate containing adulteration compounds). The lime juice samples were freeze‐dried and analyzed using FT‐IR spectroscopy. At first, principal component analysis (PCA) was applied for clustering, but the samples were not thoroughly clustered with respect to their original groups in score plots. To enhance the classification rates, different chemometric algorithms including variable importance in projection (VIP), partial least square‐discriminant analysis (PLS‐DA), and counter propagation artificial neural networks (CPANN) were used. The best discriminatory wavenumbers related to each class were selected using the VIP‐PLS‐DA algorithm. Then, the CPANN algorithm was used as a nonlinear mapping tool for classification of the samples based on their original groups. The lime juice samples were correctly designated to their original groups in CPANN maps and the overall accuracy of the model reached up to 0.96 and 0.87 for the training and validation procedures. This level of accuracy indicated the FT‐IR spectroscopy coupled with VIP‐PLS‐DA and CPANN methods can be used successfully for detection of authenticity of lime juice samples.

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D_{2} = \frac{D_{1} ‐ D_{m}}{δ_{s}}

…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detection of fraud in lime juice using pattern recognition techniques and FT‐IR spectroscopy

Mohammadian

Barzegar

Mani-Varnosfaderani

2021

Food Science & Nutrition

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Identification of molecular features necessary for selective inhibition of B cell lymphoma proteins using machine learning techniques

2018

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Selective inhibition of Bcl-2 and Bcl-x proteins due to their dual inhibition toxicity plays an important role in treatment of cancer and chemotherapy effectiveness; therefore, in the last decade, discovery of selective inhibitors for Bcl-2 and Bcl-x proteins has become a significant and important research topic. The present contribution paves the way for characterization of molecular features which induce selectivity for inhibition of Bcl-2 and Bcl-x. In this line, a total of 1534 molecules related to inhibition of Bcl-2 and Bcl-x proteins were collected from Binding Database. A diverse set of molecular descriptors was calculated for each molecule, and the best subset of descriptors were selected using variable importance in projection (VIP) approach. The molecules were classified according to their therapeutic targets (Bcl-2/Bcl-x) and activities. Partial least square-discriminate analysis (PLS-DA) and supervised Kohonen network (SKN) models were utilized to relate the molecular structures of chemicals to their activities and selectivities. According to the VIP-selected descriptors physicochemical properties, such as polarity number, number of branches, size and cyclicity of the molecule, flexibility, functional counts and constitutional descriptors, all affect the activities of Bcl-2 and Bcl-x inhibitors. The performances of PLS-DA and SKN methods were evaluated based on statistical parameters derived from the confusion matrices. The models were validated using tenfold cross-validation and an external test set. The best statistical results were obtained by implementing the SKN model. The classification rates range from 93.5 to 79.1% for the training and validation procedure for the optimized SKN models. The high values of the obtained classification rates demonstrate that the information provided in this work would be useful to design new drugs with selective inhibitory activities toward Bcl-2 or Bcl-x proteins for more effective treatment of cancer.

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Development of classification models for identification of important structural features of isoform-selective histone deacetylase inhibitors (class I)

2019

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Classification of sphingosine kinase inhibitors using counter propagation artificial neural networks: A systematic route for designing selective SphK inhibitors

Cited by 5 publications

References 52 publications

Detection of fraud in lime juice using pattern recognition techniques and FT‐IR spectroscopy

Detection of fraud in lime juice using pattern recognition techniques and FT‐IR spectroscopy

Identification of molecular features necessary for selective inhibition of B cell lymphoma proteins using machine learning techniques

Development of classification models for identification of important structural features of isoform-selective histone deacetylase inhibitors (class I)

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