Wengang Zhou

A novel quantum swarm evolutionary algorithm for solving 0-1 knapsack problem

A novel quantum swarm evolutionary algorithm is presented based on quantum-inspired evolutionary algorithm in this article. The proposed algorithm adopts quantum angle to express Q-bit and improved particle swarm optimization to update automatically. The simulated effectiveness is examined in solving 0-1 knapsack problem.

Feature selection for microarray data analysis using mutual information and rough set theory

Cancer classification is one major application of microarray data analysis. Due to the ultra high dimension of gene expression data, efficient feature selection methods are in great needs for selecting a small number of informative genes. In this paper, we propose a novel feature selection method MIRS based on mutual information and rough set. First, we select some top-ranked features which have higher mutual information with the target class to predict. Then rough set theory is applied to remove the redundancy among these selected genes. Binary particle swarm optimization (BPSO) is first proposed for attribute reduction in rough set. Finally, the effectiveness of the proposed method is evaluated by the classification accuracy of SVM classifier. Experiment results show that MIRS is superior to some other classical feature selection methods and can get higher prediction accuracy with small number of features. Generally, the results are highly promising.

Analysis of gene expression data: application of quantum-inspired evolutionary algorithm to minimum sum-of-squares clustering

Microarray experiments have produced a huge amount of gene expression data. So it becomes necessary to develop effective clustering techniques to extract the fundamental patterns inherent in the data. In this paper, we propose a novel evolutionary algorithm so called quantum-inspired evolutionary algorithm (QEA) for minimum sum-of-squares clustering. We use a new representation form and add an additional mutation operation in QEA. Experiment results show that the proposed algorithm has better global search ability and is superior to some conventional clustering algorithms such as k-means and self-organizing maps.

Feature Selection for Microarray Data Analysis Using Mutual Information and Rough Set Theory

Cancer classification is one major application of microarray data analysis. Due to the ultra high dimension of gene expression data, efficient feature selection methods are in great needs for selecting a small number of informative genes. In this paper, we propose a novel feature selection method based on mutual information and rough set (MIRS). First, we select some top-ranked features which have higher mutual information with the target class to predict. Then rough set theory is applied to remove the redundancy among these selected genes. Binary particle swarm optimization (BPSO) is first proposed for attribute reduction in rough set. Finally, the effectiveness of the proposed method is evaluated by the classification accuracy of SVM classifier. Experi-ment results show that MIRS is superior to some other classical feature selec-tion methods and can get higher prediction accuracy with small number of fea-tures. Generally, the results are highly promising.

Identification of transcription factor binding sites using hybrid particle swarm optimization

Transcription factors are key regulatory elements that control gene expression. Recognition of transcription factor binding sites (TFBS) motif from the upstream region of genes remains a highly important and unsolved problem particularly in higher eukaryotic genomes. In this paper, we present a new approach for studying this challenging issue. We first formulate the binding sites motif identification problem as a combinatorial optimization problem. Then hybrid particle swarm optimization (HPSO) is proposed for solving such a problem in upstream regions of genes regulated by octamer binding factor. We have developed two local search operators and one recombination mutation operator in HPSO. Experiment results show that the proposed algorithm is effective in obtaining known TFBS motif and can produce some putative binding sites motif. The results are highly encouraging.

A fuzzy neural network system based on generalized class cover and particle swarm optimization

A voting-mechanism-based fuzzy neural network system is proposed in this paper. When constructing the network structure, a generalized class cover problem is presented and its two solving algorithm, an improved greedy algorithm and a binary particle swarm optimization algorithm, are proposed to get the class covers with relatively even radii, which are used to partition fuzzy input space and extract fewer robust fuzzy IF-THEN rules. Meanwhile, a weighted Mamdani inference mechanism is adopted to improve the efficiency of the system output and a real-valued particle swarm optimization-based algorithm is used to refine the system parameters. Experimental results show that the system is feasible and effective.

Prediction of contact maps using modified transiently chaotic neural network

Contact maps are considered one of the most useful strategic steps in protein folding recognition. And there are a variety of measures of residues contact used in the literature. In this paper, we address our question on using a transiently chaotic neural network to predict the contact maps and whether it is reasonable. Our results show that it is more successful that we predict proteins contact maps based on modified transiently chaotic neural network.

Prediction of contact maps in proteins based on recurrent neural network with bias units

Prediction of inter_residue contact maps may be seen as a strategic step toward the solution of fundamental open problems in structural genomics. Predicting the contact map of a protein of unknown structure can give significant clues about the structure of and folding mechanism of that protein. In this paper, we focus on prediction of contact maps in proteins based on recurrent neural network with bias units and have gotten a better prediction results.

A fuzzy neural network system based on generalized class cover problem

A voting-mechanism-based fuzzy neural network system based on generalized class cover problem and particle swarm optimization is proposed in this paper. When constructing the network structure, a generalized class cover problem and an improved greedy algorithm are adopted to get the class covers with relatively even radii, which are used to partition fuzzy input space and extract fewer robust fuzzy IF-THEN rules. Meanwhile, a weighted Mamdani inference mechanism is proposed to improve the efficiency of the system output and a particle swarm optimization-based algorithm is used to refine the system parameters. Experimental results show that the system is feasible and effective.

A Study on Protein Residue Contacts Prediction by Recurrent Neural Network

A new method was described for using a recurrent neural network with bias units to predict contact maps in proteins. The main inputs to the neural network include residues pairwise, residue classification according to hydrophobicity, polar, acidic, basic and secondary structure information and residue separation between two residues. In our work, a dataset was used which was composed of 53 globulin proteins of known 3D structure. An average predictive accuracy of 0.29 was obtained. Our results demonstrate the viability of the approach for predicting contact maps.