作者机构：
[Wen Shi; Fang Wang] Science College, Hunan Agricultural University, Changsha 410128, China;[Fang Wang; Gui-ping Liao] Agricultural Information Institute, Hunan Agricultural University, Changsha 410128, China;[Xiao-yang Zhou] Department of Mathematics, Huazhong University of Science and Technology, Wuhan 430074, China
作者机构：
[Ming-shun Chen] USDA-ARS and Department of Entomology, Kansas State University, Manhattan, KS, 66506, United States;[Zheming Yuan; Zhijun Dai; Hongyan Zhang] Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization, Changsha, 410128, China;[Zheming Yuan; Zhijun Dai; Hongyan Zhang] College of Bio-safety Science and Technology, Hunan Agricultural University, Changsha, 410128, China;[Haiyan Wang] Department of Statistics, Kansas State University, Manhattan, KS, 66506, United States;[Hongyan Zhang] College of Information Science and Technology, Hunan Agricultural University, Changsha, 410128, China
通讯机构：
[Wang, HY] Kansas State Univ, Dept Stat, Manhattan, KS 66506 USA.
关键词：
Binary matrix - Cancer classification - Cancer tissues - Classification accuracy - Computationally efficient - Feature space - Gene Expression Data - Gene expression datasets - Gene interactions - Gene selection - Human cancer - Informative genes - Joint effect - Leave-one-out - Multiple classifiers - Over fitting problem - Search scheme - Search spaces - Wrapper methods
作者机构：
[ Deng, Xiaopeng ; Song, Kehui ; Ding, Xiangling ] Department of Physics and Information Engineering, Huaihua University, Huaihua 418008, China;[ Chen, Guangyi ] College of Information Science and Technology, Hunan Agricultural University, Changsha 410128, China
期刊：
IEEE Transactions on Nanobioscience,2012年11(4):386-393 ISSN：1536-1241
通讯作者：
Wang, J.(jxwang@mail.csu.edu.cn)
作者机构：
[ Wu, Fang-Xiang ] Department of Mechanical Engineering, Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada;[Wang, Jianxin ; Li, Min ; Ren, Jun ] School of Information Science and Engineering, Central SouthUniversity, Changsha, 410083, China;[ Ren, Jun ] College of Information Science and Technology, Hunan Agricultural University, Changsha, 410128, China
通讯机构：
[Wang, Jianxin] School of Information Science and Engineering, Central SouthUniversity, Changsha, 410083, China.
摘要：
A novel method, which we called the analogous multifractal cross-correlation analysis, is proposed in this paper to study the multifractal behavior in the power-law cross-correlation between price and load in California electricity market. In addition, a statistic rho(AMF-XA), which we call the analogous multifractal cross-correlation coefficient, is defined to test whether the cross-correlation between two given signals is genuine or not. Our analysis finds that both the price and load time series in California electricity market express multifractal nature. While, as indicated by the rho(AMF-XA) statistical test, there is a huge difference in the cross-correlation behavior between the years 1999 and 2000 in California electricity markets. (C) 2013 American Institute of Physics. [http://dx.doi.org/10.1063/1.4793355]
摘要：
BACKGROUND: High-throughput microarray technologies have generated and accumulated massive amounts of gene expression datasets that contain expression levels of thousands of genes under hundreds of different experimental conditions. The microarray datasets are usually presented in 2D matrices, where rows represent genes and columns represent experimental conditions. The analysis of such datasets can discover local structures composed by sets of genes that show coherent expression patterns under subsets of experimental conditions. It leads to the development of sophisticated algorithms capable of extracting novel and useful knowledge from a biomedical point of view. In the medical domain, these patterns are useful for understanding various diseases, and aid in more accurate diagnosis, prognosis, treatment planning, as well as drug discovery. RESULTS: In this work we present the CMOPSOB (Crowding distance based Multi-objective Particle Swarm Optimization Biclustering), a novel clustering approach for microarray datasets to cluster genes and conditions highly related in sub-portions of the microarray data. The objective of biclustering is to find sub-matrices, i.e. maximal subgroups of genes and subgroups of conditions where the genes exhibit highly correlated activities over a subset of conditions. Since these objectives are mutually conflicting, they become suitable candidates for multi-objective modelling. Our approach CMOPSOB is based on a heuristic search technique, multi-objective particle swarm optimization, which simulates the movements of a flock of birds which aim to find food. In the meantime, the nearest neighbour search strategies based on crowding distance and -dominance can rapidly converge to the Pareto front and guarantee diversity of solutions. We compare the potential of this methodology with other biclustering algorithms by analyzing two common and public datasets of gene expression profiles. In all cases our method can find localized structures related to sets of genes that show consistent expression patterns across subsets of experimental conditions. The mined patterns present a significant biological relevance in terms of related biological processes, components and molecular functions in a species-independent manner. CONCLUSION: The proposed CMOPSOB algorithm is successfully applied to biclustering of microarray dataset. It achieves a good diversity in the obtained Pareto front, and rapid convergence. Therefore, it is a useful tool to analyze large microarray datasets.
摘要：
Background: In this paper, a novel method is proposed to identify plant species by using the two-dimensional multifractal detrended fluctuation analysis (2D MF-DFA). Our method involves calculating a set of multifractal parameters that characterize the texture features of each plant leaf image. An index, I-0, that characterizes the relation of the intra-species variances and inter-species variances is introduced. This index is used to select three multifractal parameters for the identification process. The procedure is applied to the Swedish leaf data set containing leaves from fifteen different tree species. Results: The chosen three parameters form a three-dimensional space in which the samples from the same species can be clustered together and be separated from other species. Support vector machines and kernel methods are employed to assess the identification accuracy. The resulting averaged discriminant accuracy reaches 98.4% for every two species by the 10 - fold cross validation, while the accuracy reaches 93.96% for all fifteen species. Conclusions: Our method, based on the 2D MF-DFA, provides a feasible and efficient procedure to identify plant species.
摘要：
Most computational methods for identifying essential proteins focus on the topological centrality of protein-protein interaction (PPI) networks. However, these methods have limitations, such as the difficulty for identifying essential proteins with low centrality values and the poor performance for incomplete PPI network. In this paper, protein complex is proven to be an important factor for determining protein essentiality and a new centrality measure, complex centrality, is proposed. The weighted average of complex centrality and subgraph centrality, called harmonic centrality (HC), is proposed to predict essential proteins. It combines PPI network topology and protein complex information and has better performance than methods based on PPI network. The improvement is higher when the PPI network is incomplete. Furthermore, a weighted PPI network is generated by integrating cellular localisation and biological process to a PPI network. The performance of HC measure is improved 5% in this weighted PPI network.
期刊：
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE,2014年28(3) ISSN：0218-0014
通讯作者：
Li, ZS
作者机构：
[Wang, Fang] College of Science, Hunan Agricultural University, Changsha 410128, China;[Li, Zong-Shou] College of Information Science and Engineering, Jishou University, Jishou 416000, China;[Wang, Fang; Liao, Gui-Ping] Agricultural Information Institute, Hunan Agricultural University, Changsha 410128, China
摘要：
BACKGROUND: One of the challenges in classification of cancer tissue samples based on gene expression data is to establish an effective method that can select a parsimonious set of informative genes. The Top Scoring Pair (TSP), k-Top Scoring Pairs (k-TSP), Support Vector Machines (SVM), and prediction analysis of microarrays (PAM) are four popular classifiers that have comparable performance on multiple cancer datasets. SVM and PAM tend to use a large number of genes and TSP, k-TSP always use even number of genes. In addition, the selection of distinct gene pairs in k-TSP simply combined the pairs of top ranking genes without considering the fact that the gene set with best discrimination power may not be the combined pairs. The k-TSP algorithm also needs the user to specify an upper bound for the number of gene pairs. Here we introduce a computational algorithm to address the problems. The algorithm is named Chisquare-statistic-based Top Scoring Genes (Chi-TSG) classifier simplified as TSG. RESULTS: The TSG classifier starts with the top two genes and sequentially adds additional gene into the candidate gene set to perform informative gene selection. The algorithm automatically reports the total number of informative genes selected with cross validation. We provide the algorithm for both binary and multi-class cancer classification. The algorithm was applied to 9 binary and 10 multi-class gene expression datasets involving human cancers. The TSG classifier outperforms TSP family classifiers by a big margin in most of the 19 datasets. In addition to improved accuracy, our classifier shares all the advantages of the TSP family classifiers including easy interpretation, invariant to monotone transformation, often selects a small number of informative genes allowing follow-up studies, resistant to sampling variations due to within sample operations. CONCLUSIONS: Redefining the scores for gene set and the classification rules in TSP family classifiers by incorporating the sample size information can lead to better selection of informative genes and classification accuracy. The resulting TSG classifier offers a useful tool for cancer classification based on numerical molecular data.
摘要：
When the number of the mixed signals is less than that of the source signals, the underdetermined blind source separation (BSS) is a significant difficult problem. Due to the fact that the great amount data of speech communications and real-time communication has been required, we utilize the intractability of the underdetermined BSS problem to present a dual key speech encryption method. The original speech is mixed with dual key signals which consist of random key signals (one-time pad) generated by secret seed and chaotic signals generated from chaotic system. In the decryption process, approximate calculation is used to recover the original speech signals. The proposed algorithm for speech signals encryption can resist traditional attacks against the encryption system, and owing to approximate calculation, decryption becomes faster and more accurate. It is demonstrated that the proposed method has high level of security and can recover the original signals quickly and efficiently yet maintaining excellent audio quality.
期刊：
BMC Systems Biology,2013年7(04):1-15 ISSN：1752-0509
通讯作者：
Wang, JX
作者机构：
[Lusheng Wang] City Univ Hong Kong, Dept Comp Sci, Kowloon, Hong Kong, Peoples R China.;[Jianxin Wang; Min Li; Jun Ren] Cent S Univ, Sch Informat Sci & Engn, Changsha 410083, Hunan, Peoples R China.;[Jun Ren] Hunan Agr Univ, Coll Informat Sci & Technol, Changsha 410128, Hunan, Peoples R China.
通讯机构：
[Wang, JX] Cent S Univ, Sch Informat Sci & Engn, Changsha 410083, Hunan, Peoples R China.
摘要：
Background: Identifying protein complexes is crucial to understanding principles of cellular organization and functional mechanisms. As many evidences have indicated that the subgraphs with high density or with high modularity in PPI network usually correspond to protein complexes, protein complexes detection methods based on PPI network focused on subgraph's density or its modularity in PPI network. However, dense subgraphs may have low modularity and subgraph with high modularity may have low density, which results that protein complexes may be subgraphs with low modularity or with low density in the PPI network. As the density-based methods are difficult to mine protein complexes with low density, and the modularity-based methods are difficult to mine protein complexes with low modularity, both two methods have limitation for identifying protein complexes with various density and modularity. Results: To identify protein complexes with various density and modularity, including those have low density but high modularity and those have low modularity but high density, we define a novel subgraph's fitness, f(rho), as f(rho)= (density)(rho)*(modularity)(1-rho), and propose a novel algorithm, named LF_PIN, to identify protein complexes by expanding seed edges to subgraphs with the local maximum fitness value. Experimental results of LF-PIN in S. cerevisiae show that compared with the results of fitness equal to density (rho = 1) or equal to modularity (rho = 0), the LF-PIN identifies known protein complexes more effectively when the fitness value is decided by both density and modularity (0<rho<1). Compared with the results of seven competing protein complex detection methods (CMC, Core-Attachment, CPM, DPClus, HC-PIN, MCL, and NFC) in S. cerevisiae and E. coli, LF-PIN outperforms other seven methods in terms of matching with known complexes and functional enrichment. Moreover, LF-PIN has better performance in identifying protein complexes with low density or with low modularity. Conclusions: By considering both the density and the modularity, LF-PIN outperforms other protein complexes detection methods that only consider density or modularity, especially in identifying known protein complexes with low density or low modularity.
作者机构：
[Wang, Fang] College of Science, Hunan Agricultural University, Changsha, China;[Li, Zong-shou] College of Information Science and Engineering, Jishou University, Jishou, China;[Li, Jin-wei] Agricultural Information Institute, Hunan Agricultural University, Changsha, China
摘要：
Feature extraction plays a great important role in image processing and pattern recognition. As a power tool, multifractal theory is recently employed for this job. However, traditional multifractal methods are proposed to analyze the objects with stationary measure and cannot for non-stationary measure. The works of this paper is twofold. First, the definition of stationary image and 2D image feature detection methods are proposed. Second, a novel feature extraction scheme for non-stationary image is proposed by local multifractal detrended fluctuation analysis (Local MF-DFA), which is based on 2D MF-DFA. A set of new multifractal descriptors, called local generalized Hurst exponent (Lh<inf>q</inf>) is defined to characterize the local scaling properties of textures. To test the proposed method, both the novel texture descriptor and other two multifractal indicators, namely, local Hölder coefficients based on capacity measure and multifractal dimension D<inf>q</inf> based on multifractal differential box-counting (MDBC) method, are compared in segmentation experiments. The first experiment indicates that the segmentation results obtained by the proposed Lh<inf>q</inf> are better than the MDBC-based D<inf>q</inf> slightly and superior to the local Hölder coefficients significantly. The results in the second experiment demonstrate that the Lh<inf>q</inf> can distinguish the texture images more effectively and provide more robust segmentations than the MDBC-based D<inf>q</inf> significantly.
作者：
Junwan Liu;Zhoujun Li;Xiaohua Hu;Yiming Chen;Feifei Liu
期刊：
BMC GENOMICS,2012年13(03):1-11 ISSN：1471-2164
通讯作者：
Hu, XH
作者机构：
[Junwan Liu] Cent S Univ Forestry & Technol, Sch Comp & Informat Engn, Changsha 410004, Hunan, Peoples R China.;[Yiming Chen] Hunan Agr Univ, Sch Informat Sci & Technol, Changsha 410128, Hunan, Peoples R China.;[Zhoujun Li] Beihang Univ, State Key Lab Software Dev Environm, Beijing 100191, Peoples R China.;[Zhoujun Li] Beihang Univ, Beijing Key Lab Network Technol, Beijing 100191, Peoples R China.;[Xiaohua Hu] Cent China Normal Univ, Dept Comp Sci, Wuhan 430079, Peoples R China.
通讯机构：
[Hu, XH] Cent China Normal Univ, Dept Comp Sci, Wuhan 430079, Peoples R China.
摘要：
BACKGROUND: Multi-objective optimization (MOO) involves optimization problems with multiple objectives. Generally, theose objectives is used to estimate very different aspects of the solutions, and these aspects are often in conflict with each other. MOO first gets a Pareto set, and then looks for both commonality and systematic variations across the set. For the large-scale data sets, heuristic search algorithms such as EA combined with MOO techniques are ideal. Newly DNA microarray technology may study the transcriptional response of a complete genome to different experimental conditions and yield a lot of large-scale datasets. Biclustering technique can simultaneously cluster rows and columns of a dataset, and hlep to extract more accurate information from those datasets. Biclustering need optimize several conflicting objectives, and can be solved with MOO methods. As a heuristics-based optimization approach, the particle swarm optimization (PSO) simulate the movements of a bird flock finding food. The shuffled frog-leaping algorithm (SFL) is a population-based cooperative search metaphor combining the benefits of the local search of PSO and the global shuffled of information of the complex evolution technique. SFL is used to solve the optimization problems of the large-scale datasets. RESULTS: This paper integrates dynamic population strategy and shuffled frog-leaping algorithm into biclustering of microarray data, and proposes a novel multi-objective dynamic population shuffled frog-leaping biclustering (MODPSFLB) algorithm to mine maximum bicluesters from microarray data. Experimental results show that the proposed MODPSFLB algorithm can effectively find significant biological structures in terms of related biological processes, components and molecular functions. CONCLUSIONS: The proposed MODPSFLB algorithm has good diversity and fast convergence of Pareto solutions and will become a powerful systematic functional analysis in genome research.
期刊：
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS,2014年17(4):1413-1425 ISSN：1386-7857
通讯作者：
Tang, Xiaoyong
作者机构：
[Tang, Xiaoyong ] State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China;[ Li, Kenli ; Tang, Xiaoyong ] School of Information Science and Engineering, Hunan University, Changsha, China;[ Liao, Guiping ] Information Science and Technology College, Hunan Agricultural University, Changsha, China
摘要：
In large-scale heterogeneous cluster computing systems, processor and network failures are inevitable and can have an adverse effect on applications executing on such systems. One way of taking failures into account is to employ a reliable scheduling algorithm. However, most existing scheduling algorithms for precedence constrained tasks in heterogeneous systems only consider scheduling length, and not efficiently satisfy the reliability requirements of task. In recognition of this problem, we build an application reliability analysis model based on Weibull distribution, which can dynamically measure the reliability of task executing on heterogeneous cluster with arbitrary networks architectures. Then, we propose a reliability-driven earliest finish time with duplication scheduling algorithm (REFTD) which incorporates task reliability overhead into scheduling. Furthermore, to improve system reliability, it duplicates task as if task hazard rate is more than threshold θ. The comparison study, based on both randomly generated graphs and the graphs of some real applications, shows that our scheduling algorithm can shorten schedule length and improve system reliability significantly.
期刊：
Computers and Electrical Engineering,2015年46:528-538 ISSN：0045-7906
通讯作者：
Zhou, HY
作者机构：
[Liu, Bo; Fu, Zigang; Zhou, Haoyu; Jiang, Wen; Shen, Yue] College of Information Science and Technology, Hunan Agricultural University, Changsha, China;[Jiang, Wen] School of Software, Hunan Vocational College of Science and Technology, Changsha, China
摘要：
In this paper, a Canny operator-based method using PCNN (Pulse-Coupled Neural Network) is proposed for color image segmentation. The coarse location information of the salient object and the background is first estimated based on the distribution of the detected key-points. An image is then over-segmented into super-pixels and their histograms are computed. The saliency of a super-pixel is obtained according to the maximum of variance ration and Shannon entropy. Color image segmentation is implemented using PCNN based on Canny operator edge detection method. The proposed method is compared with state-of-the-art methods on the widely used dataset, and the experiments show that it overall obtains more accurate results. (C) 2015 Elsevier Ltd. All rights reserved.
