QSAR analysis of tyrosine kinase inhibitor using modified ant colony optimization and multiple linear regression.

Quantitative structure-activity relationship (QSAR) models of inhibiting action of some analogues of 4-(3-bromoanilino)-6,7-dimethoxyquinazoline on epidermal growth factor receptor tyrosine kinase were constructed using modified ant colony optimization (ACO) method. As a comparison to this method, the evolutionary algorithm (EA) was also tested. It has been demonstrated that the modified ACO is a useful tool for variable selection comparable to EA. In the selected descriptors, electronic descriptor sigma(Y)(-) is the most important descriptor in predicting EGFR inhibitory activity. Electron-donating groups such as Y-substituents enhance the activity as evident by negative sigma(Y)(-). In addition, for quinazoline substituents, nitro group has a large deactivating effect.

Particle swarm algorithm trained neural network for QSAR studies of inhibitors of platelet-derived growth factor receptor phosphorylation.

The multilayer feed-forward artificial neural network (ANN) has been widely used in QSAR studying. Back-propagation algorithm (BP) and the use of evolutionary search as an ANN training method has some limitations associated with overfitting, local optimum problems and slow convergence rate. In this paper, particle swarm optimization (PSO) as a relatively new optimization technique has been used in ANN training. Compared to ANN trained by BP algorithm and evolutionary search, ANN training by PSO algorithm (PSONN) show satisfactory performance, converges quickly towards the optimal position and can avoid overfitting in some extent. The PSONN has been testified by using in QSAR modeling for inhibitory activity of 4-[4-(N-substituted (thio) carbamoyl)-1-piperazinyl]-6,7-dimethoxyquinazoline derivatives.

Simultaneous genes and training samples selection by modified particle swarm optimization for gene expression data classification.

Gene expression datasets is a means to classify and predict the diagnostic categories of a patient. Informative genes and representative samples selection are two important aspects for reducing gene expression data. Identifying and pruning redundant genes and samples simultaneously can improve the performance of classification and circumvent the local optima problem. In the present paper, the modified particle swarm optimization was applied to selecting optimal genes and samples simultaneously and support vector machine was used as an objective function to determine the optimum set of genes and samples. To evaluate the performance of the new proposed method, it was applied to three publicly available microarray datasets. It has been demonstrated that the proposed method for gene and sample selection is a useful tool for mining high dimension data.

A combination of modified particle swarm optimization algorithm and support vector machine for gene selection and tumor classification.

In the analysis of gene expression profiles, the number of tissue samples with genes expression levels available is usually small compared with the number of genes. This can lead either to possible overfitting or even to a complete failure in analysis of microarray data. The selection of genes that are really indicative of the tissue classification concerned is becoming one of the key steps in microarray studies. In the present paper, we have combined the modified discrete particle swarm optimization (PSO) and support vector machines (SVM) for tumor classification. The modified discrete PSO is applied to select genes, while SVM is used as the classifier or the evaluator. The proposed approach is used to the microarray data of 22 normal and 40 colon tumor tissues and showed good prediction performance. It has been demonstrated that the modified PSO is a useful tool for gene selection and mining high dimension data.