Gui-Fang Shao

Improving the Computational Efficiency of Recursive Cluster Elimination for Gene Selection

The gene expression data are usually provided with a large number of genes and a relatively small number of samples, which brings a lot of new challenges. Selecting those informative genes becomes the main issue in microarray data analysis. Recursive cluster elimination based on support vector machine (SVM-RCE) has shown the better classification accuracy on some microarray data sets than recursive feature elimination based on support vector machine (SVM-RFE). However, SVM-RCE is extremely time-consuming. In this paper, we propose an improved method of SVM-RCE called ISVM-RCE. ISVM-RCE first trains a SVM model with all clusters, then applies the infinite norm of weight coefficient vector in each cluster to score the cluster, finally eliminates the gene clusters with the lowest score. In addition, ISVM-RCE eliminates genes within the clusters instead of removing a cluster of genes when the number of clusters is small. We have tested ISVM-RCE on six gene expression data sets and compared their performances with SVM-RCE and linear-discriminant-analysis-based RFE (LDA-RFE). The experiment results on these data sets show that ISVM-RCE greatly reduces the time cost of SVM-RCE, meanwhile obtains comparable classification performance as SVM-RCE, while LDA-RFE is not stable.

Using the Maximum Between-Class Variance for Automatic Gridding of cDNA Microarray Images

Gridding is the first and most important step to separate the spots into distinct areas in microarray image analysis. Human intervention is necessary for most gridding methods, even if some so-called fully automatic approaches also need preset parameters. The applicability of these methods is limited in certain domains and will cause variations in the gene expression results. In addition, improper gridding, which is influenced by both the misalignment and high noise level, will affect the high throughput analysis. In this paper, we have presented a fully automatic gridding technique to break through the limitation of traditional mathematical morphology gridding methods. First, a preprocessing algorithm was applied for noise reduction. Subsequently, the optimal threshold was gained by using the improved Otsu method to actually locate each spot. In order to diminish the error, the original gridding result was optimized according to the heuristic techniques by estimating the distribution of the spots. Intensive experiments on six different data sets indicate that our method is superior to the traditional morphology one and is robust in the presence of noise. More importantly, the algorithm involved in our method is simple. Furthermore, human intervention and parameters presetting are unnecessary when the algorithm is applied in different types of microarray images.

Structural optimization of Pt-Pd-Au trimetallic nanoparticles by discrete particle swarm algorithms

Trimetallic nanoparticles have received enormous attention due to their multifunctional catalytic activities. Their surface structures strongly determine their catalytic performances, therefore an investigation on their stable structures is of great importance for understanding the catalytic activity. In this article, we have employed an improved discrete particle swarm optimization algorithm to systematically explore the structural stability and segregation behavior of tetrahexahedral Pt–Pd–Au trimetallic nanoparticles. The exchange probability was introduced to decrease computational cost and to avoid falling into local optima. The simulation results reveal that Pt atoms tend to occupy the interior, while both Pd and Au atoms preferentially segregate to the surface. Furthermore, Au atoms exhibit stronger surface segregation than Pd ones, and the segregative behavior is less pronounced in larger nanoparticles. Besides, the distribution of surface atoms has been further examined by the analyses of coordination number. This study provides a fundamental perspective on structural features and segregation behavior of trimetallic nanoparticles.

Structural optimization of Pt–Pd alloy nanoparticles using an improved discrete particle swarm optimization algorithm

Abstract Pt–Pd alloy nanoparticles, as potential catalyst candidates for new-energy resources such as fuel cells and lithium ion batteries owing to their excellent reactivity and selectivity, have aroused growing attention in the past years. Since structure determines physical and chemical properties of nanoparticles, the development of a reliable method for searching the stable structures of Pt–Pd alloy nanoparticles has become of increasing importance to exploring the origination of their properties. In this article, we have employed the particle swarm optimization algorithm to investigate the stable structures of alloy nanoparticles with fixed shape and atomic proportion. An improved discrete particle swarm optimization algorithm has been proposed and the corresponding scheme has been presented. Subsequently, the swap operator and swap sequence have been applied to reduce the probability of premature convergence to the local optima. Furthermore, the parameters of the exchange probability and the ‘particle’ size have also been considered in this article. Finally, tetrahexahedral Pt–Pd alloy nanoparticles has been used to test the effectiveness of the proposed method. The calculated results verify that the improved particle swarm optimization algorithm has superior convergence and stability compared with the traditional one.

An improved K-means clustering method for cDNA microarray image segmentation.

Microarray technology is a powerful tool for human genetic research and other biomedical applications. Numerous improvements to the standard K-means algorithm have been carried out to complete the image segmentation step. However, most of the previous studies classify the image into two clusters. In this paper, we propose a novel K-means algorithm, which first classifies the image into three clusters, and then one of the three clusters is divided as the background region and the other two clusters, as the foreground region. The proposed method was evaluated on six different data sets. The analyses of accuracy, efficiency, expression values, special gene spots, and noise images demonstrate the effectiveness of our method in improving the segmentation quality.

A multi-populations multi-strategies differential evolution algorithm for structural optimization of metal nanoclusters

Abstract Theoretically, the determination of the structure of a cluster is to search the global minimum on its potential energy surface. The global minimization problem is often nondeterministic-polynomial-time (NP) hard and the number of local minima grows exponentially with the cluster size. In this article, a multi-populations multi-strategies differential evolution algorithm has been proposed to search the globally stable structure of Fe and Cr nanoclusters. The algorithm combines a multi-populations differential evolution with an elite pool scheme to keep the diversity of the solutions and avoid prematurely trapping into local optima. Moreover, multi-strategies such as growing method in initialization and three differential strategies in mutation are introduced to improve the convergence speed and lower the computational cost. The accuracy and effectiveness of our algorithm have been verified by comparing the results of Fe clusters with Cambridge Cluster Database. Meanwhile, the performance of our algorithm has been analyzed by comparing the convergence rate and energy evaluations with the classical DE algorithm. The multi-populations, multi-strategies mutation and growing method in initialization in our algorithm have been considered respectively. Furthermore, the structural growth pattern of Cr clusters has been predicted by this algorithm. The results show that the lowest-energy structure of Cr clusters contains many icosahedra, and the number of the icosahedral rings rises with increasing size.

A Combinational Clustering Based Method for cDNA Microarray Image Segmentation

Microarray technology plays an important role in drawing useful biological conclusions by analyzing thousands of gene expressions simultaneously. Especially, image analysis is a key step in microarray analysis and its accuracy strongly depends on segmentation. The pioneering works of clustering based segmentation have shown that k-means clustering algorithm and moving k-means clustering algorithm are two commonly used methods in microarray image processing. However, they usually face unsatisfactory results because the real microarray image contains noise, artifacts and spots that vary in size, shape and contrast. To improve the segmentation accuracy, in this article we present a combination clustering based segmentation approach that may be more reliable and able to segment spots automatically. First, this new method starts with a very simple but effective contrast enhancement operation to improve the image quality. Then, an automatic gridding based on the maximum between-class variance is applied to separate the spots into independent areas. Next, among each spot region, the moving k-means clustering is first conducted to separate the spot from background and then the k-means clustering algorithms are combined for those spots failing to obtain the entire boundary. Finally, a refinement step is used to replace the false segmentation and the inseparable ones of missing spots. In addition, quantitative comparisons between the improved method and the other four segmentation algorithms--edge detection, thresholding, k-means clustering and moving k-means clustering--are carried out on cDNA microarray images from six different data sets. Experiments on six different data sets, 1) Stanford Microarray Database (SMD), 2) Gene Expression Omnibus (GEO), 3) Baylor College of Medicine (BCM), 4) Swiss Institute of Bioinformatics (SIB), 5) Joe DeRisi’s individual tiff files (DeRisi), and 6) University of California, San Francisco (UCSF), indicate that the improved approach is more robust and sensitive to weak spots. More importantly, it can obtain higher segmentation accuracy in the presence of noise, artifacts and weakly expressed spots compared with the other four methods.

Noise Estimation and Reduction in Microarray Images

Lots of error sources affect the microarray image quality, especially the noise. An image may contain different type noises which will produce distinct influence on image processing, so it doesn’t need to remove all. This paper analyzed the affection of different noises to automatic gridding and proposed grid line number for quantitive evaluation. A new algorithm for noise reduction was developed, which included two parts: edge noise reduction and highly fluorescence noise reduction. Edge detection was executed on the vertical and horizontal projections of microarray image. Highly fluorescent noise was removed by linear replace, which is an easy and fast means. The algorithm was implemented and compared to other common noise reduction methods. Experiment results show the feasibility of the proposed approach.

Genetic Algorithm for Building Optimization: State-of-the-Art Survey

Model-based building operation optimization can be used to reduce building energy consumption, so as to improve the indoor environment quality. Genetic Algorithm (GA) is one of the commonly used optimization algorithms for building applications. To provide readers up-to-date information, this paper attempts to summarize recent researches on building optimization with GA. Firstly, the principle of GA is introduced. Then, we summarize the literatures according to different categories, including applied system types and optimization objectives. We also provide some insights into the parameter setting and operator selection for GA. This review paper intends to give a better understanding and some future directions for building research community on how to apply GA for building energy optimization.

Structural optimization and segregation behavior of quaternary alloy nanoparticles based on simulated annealing algorithm

Alloy nanoparticles exhibit higher catalytic activity than monometallic nanoparticles, and their stable structures are of importance to their applications. We employ the simulated annealing algorithm to systematically explore the stable structure and segregation behavior of tetrahexahedral Pt–Pd–Cu–Au quaternary alloy nanoparticles. Three alloy nanoparticles consisting of 443 atoms, 1417 atoms, and 3285 atoms are considered and compared. The preferred positions of atoms in the nanoparticles are analyzed. The simulation results reveal that Cu and Au atoms tend to occupy the surface, Pt atoms preferentially occupy the middle layers, and Pd atoms tend to segregate to the inner layers. Furthermore, Au atoms present stronger surface segregation than Cu ones. This study provides a fundamental understanding on the structural features and segregation phenomena of multi-metallic nanoparticles.