Self-Configuring Radial Basis Function Neural Networks for Chemical Pattern Recognition

and, Chuanhao Wan; Harrington, Peter de B.

doi:10.1021/ci990306t

Cited by 48 publications

(24 citation statements)

References 30 publications

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“…The RBFN can also be trained by back-propagation. Wan and Harrington developed a type of RBFN that is a self-configuring radial basis function network (SCRBFN) [12]. In a SCRBFN, a linear averaging (LA) clustering algorithm is applied to determine the parameters of the hidden units.…”

Section: Open Accessmentioning

confidence: 99%

Radial Basis Function Cascade Correlation Networks

Harrington

2009

Algorithms

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A cascade correlation learning architecture has been devised for the first time for radial basis function processing units. The proposed algorithm was evaluated with two synthetic data sets and two chemical data sets by comparison with six other standard classifiers. The ability to detect a novel class and an imbalanced class were demonstrated with synthetic data. In the chemical data sets, the growth regions of Italian olive oils were identified by their fatty acid profiles; mass spectra of polychlorobiphenyl compounds were classified by chlorine number. The prediction results by bootstrap Latin partition indicate that the proposed neural network is useful for pattern recognition.

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Section: Open Accessmentioning

confidence: 99%

Radial Basis Function Cascade Correlation Networks

Harrington

2009

Algorithms

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“…RBFNN is a type of neural networks used to solve several problems such as modeling and classification [41].…”

Section: Theory Of Regression Analysis and Rbfnnmentioning

confidence: 99%

Accurate Prediction of the Folding Rate for Two‐State Proteins Based on Amino Acid Sequences

Zhao

Zhang

et al. 2007

QSAR Comb. Sci.

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One of the most important challenges in biology is to understand and simulate the folding behavior of some simple two-state proteins. In this work, a large pool for descriptors of proteins was developed including amino acid descriptors, topological descriptors and charged partial surface area descriptors. A heuristic method was employed to select significant features. As a result, three descriptors, total contact distance, unfolding entropy change and total charge weighted partial negative surface area were chosen. Total contact distance was proved to be the most relevant factor controlling the folding behavior. Based on these descriptors, a support vector machine method was applied to build a predictive model. Comparing the statistical results of other works and methods, support vector machine method exhibited the best whole performance. Our results demonstrate that the native-state topology is the major determinant for the folding rates of two-state proteins and that the support vector machine method is a powerful tool to build a predictive model. In turn, the method and the knowledge can be used to develop in-silico predictive models to simulate the folding process of proteins.

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“…The theory of neural networks has been described in detail in many reviews already [30,85,86]. Recent developments focus on improvements of self-organizing neural networks for chemical feature classification [87,88]. In addition to neural networks, kernel methods have been increasingly applied to large-scale data analysis.…”

Section: Model Estimationmentioning

confidence: 99%

Status of HTS Data Mining Approaches

2004

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ReviewHigh-throughput screening of large compound collections results in large sets of data. This review gives an overview of the most frequently employed computational techniques for the analysis of such data and the establishment of first QSAR models. Various methods for descriptor selection, classification and data mining are discussed. Recent trends include the application of kernel-based machine learning methods for the design of focused libraries and compilation of target-family biased compound collections.

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Self-Configuring Radial Basis Function Neural Networks for Chemical Pattern Recognition

Cited by 48 publications

References 30 publications

Radial Basis Function Cascade Correlation Networks

Radial Basis Function Cascade Correlation Networks

Accurate Prediction of the Folding Rate for Two‐State Proteins Based on Amino Acid Sequences

Status of HTS Data Mining Approaches

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