作者机构：
[Xiao-yang Zhou] Department of Mathematics, Huazhong University of Science and Technology, Wuhan 430074, China;[Gui-ping Liao; Fang Wang] Agricultural Information Institute, Hunan Agricultural University, Changsha 410128, China;[Fang Wang; Wen Shi] Science College, Hunan Agricultural University, Changsha 410128, China
关键词：
Cross correlations - Detrended cross-correlation analysis - Load-time series - Long-term correlations - Multi fractals - Multifractal theory - Operating data - Power markets
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.
作者机构：
[Hongyan Zhang; Zhijun Dai; Zheming Yuan] Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization, Changsha, 410128, China;[Hongyan Zhang; Zhijun Dai; Zheming Yuan] College of Bio-safety Science and Technology, Hunan Agricultural University, Changsha, 410128, China;[Hongyan Zhang] College of Information Science and Technology, Hunan Agricultural University, Changsha, 410128, China;[Haiyan Wang] Department of Statistics, Kansas State University, Manhattan, KS, 66506, United States;[Ming-shun Chen] USDA-ARS and Department of Entomology, Kansas State University, Manhattan, KS, 66506, United States
通讯机构：
[Wang, Haiyan] 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
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.
作者机构：
[Ding, Xiangling; Song, Kehui; Deng, Xiaopeng] 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
作者：
Junwan Liu;Zhoujun Li;Xiaohua Hu;Yiming Chen;Feifei Liu
期刊：
BMC GENOMICS,2012年13(S3):S6- ISSN：1471-2164
通讯作者：
Hu, Xiaohua
作者机构：
[Junwan Liu] Central South University of Forestry and Technology;[Zhoujun Li; Xiaohua Hu] Beihang University;[Yiming Chen] Central China Normal University;[Feifei Liu] Drexel University
通讯机构：
[Hu, Xiaohua] Cent China Normal Univ, Dept Comp Sci, Wuhan 430079, Peoples R China.
会议名称：
IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
会议时间：
NOV 12-15, 2011
会议地点：
Atlanta, GA
会议主办单位：
[Hu, Xiaohua] Cent China Normal Univ, Dept Comp Sci, Wuhan 430079, Peoples R China.^[Liu, Junwan] Cent S Univ Forestry & Technol, Sch Comp & Informat Engn, Changsha 410004, Hunan, Peoples R China.^[Li, Zhoujun] Beihang Univ, State Key Lab Software Dev Environm, Beijing 100191, Peoples R China.^[Li, Zhoujun] Beihang Univ, Beijing Key Lab Network Technol, Beijing 100191, Peoples R China.^[Hu, Xiaohua] Drexel Univ, Coll Informat Sci, Philadelphia, PA 19104 USA.^[Chen, Yiming] Hunan Agr Univ, Sch Informat Sci & Technol, Changsha 410128, Hunan, 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.
作者：
Junwan Liu;Zhoujun Li;Xiaohua Hu;Yiming Chen;EK Park
期刊：
BMC GENOMICS,2011年12(S2):S11- ISSN：1471-2164
通讯作者：
Liu, Junwan
作者机构：
[Xiaohua Hu] Drexel Univ, Coll Informat Sci & Technol, Philadelphia, PA 19104 USA.;[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.;[Yiming Chen] Hunan Agr Univ, Sch Informat Sci & Technol, Changsha 410128, Hunan, Peoples R China.;[Junwan Liu] Cent S Univ Forestry & Technol, Sch Comp & Informat Engn, Changsha 410004, Hunan, Peoples R China.
通讯机构：
[Liu, Junwan] Cent S Univ Forestry & Technol, Sch Comp & Informat Engn, Changsha 410004, Hunan, Peoples R China.
会议名称：
IEEE International Conference on Bioinformatics and Biomedicine
会议时间：
DEC 18-21, 2010
会议地点：
Hong Kong, PEOPLES R CHINA
会议主办单位：
[Liu, Junwan] Cent S Univ Forestry & Technol, Sch Comp & Informat Engn, Changsha 410004, Hunan, Peoples R China.^[Li, Zhoujun] Beihang Univ, State Key Lab Software Dev Environm, Beijing 100191, Peoples R China.^[Li, Zhoujun] BeiHang Univ, Beijing Key Lab Network Technol, Beijing 100191, Peoples R China.^[Hu, Xiaohua] Drexel Univ, Coll Informat Sci & Technol, Philadelphia, PA 19104 USA.^[Chen, Yiming] Hunan Agr Univ, Sch Informat Sci & Technol, Changsha 410128, Hunan, Peoples R China.^[Park, E. K.] CSI CUNY, Staten Isl, NY USA.
摘要：
BACKGROUND: Newly microarray technologies yield large-scale datasets. The microarray datasets are usually presented in 2D matrices, where rows represent genes and columns represent experimental conditions. Systematic analysis of those datasets provides the increasing amount of information, which is urgently needed in the post-genomic era. Biclustering, which is a technique developed to allow simultaneous clustering of rows and columns of a dataset, might be useful to extract more accurate information from those datasets. Biclustering requires the optimization of two conflicting objectives (residue and volume), and a multi-objective artificial immune system capable of performing a multi-population search. As a heuristic search technique, artificial immune systems (AISs) can be considered a new computational paradigm inspired by the immunological system of vertebrates and designed to solve a wide range of optimization problems. During biclustering several objectives in conflict with each other have to be optimized simultaneously, so multi-objective optimization model is suitable for solving biclustering problem. RESULTS: Based on dynamic population, this paper proposes a novel dynamic multi-objective immune optimization biclustering (DMOIOB) algorithm to mine coherent patterns from microarray data. Experimental results on two common and public datasets of gene expression profiles show that our approach can effectively find significant 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. CONCLUSIONS: The proposed DMOIOB algorithm is an efficient tool to analyze large microarray datasets. It achieves a good diversity and rapid convergence.
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：
[Ren, Jun] College of Information Science and Technology, Hunan Agricultural University, Changsha, 410128, China;[Ren, Jun; Li, Min; Wang, Jianxin] School of Information Science and Engineering, Central SouthUniversity, Changsha, 410083, China;[Wu, Fang-Xiang] Department of Mechanical Engineering, Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada
通讯机构：
[Wang, Jianxin] Cent S Univ, Sch Informat Sci & Engn, Changsha 410083, Hunan, Peoples R 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]
期刊：
Journal of Intelligent and Fuzzy Systems,2016年31(2):1079-1088 ISSN：1064-1246
通讯作者：
Yang, Xiaopan(994700757@qq.com)
作者机构：
[Zhu, Xinghui; Yang, Xiaopan; Tang, Xiaoyong; Liao, Guiping] Information Science and Technology College, Southern Regional Collaborative Innovation Center for Grain and Oil Crops in China, Hunan Agricultural University, Changsha, 410128, China
期刊：
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),2005年3759:647-656 ISSN：0302-9743
通讯作者：
Yu, F.(yufei@hunau.net)
作者机构：
[Dai, Xiao-Peng; Huang, Hunag; Yu, Fei] Institute of Computer and Information Engineering, Hunan Agricultural University, Changsha 410128, Hunan, China;[Xu, Cheng] College of Computer and Communication, Hunan University, Changsha 410082, Hunan, China;[Zhu, Miao-Liang; Yu, Fei] Institute of Artificial Intelligence, Zhejiang University, Hangzhou 310027, Zhejiang, China
期刊：
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),2006年3842:869-876 ISSN：0302-9743
通讯作者：
Yu, F
作者机构：
[Shen, Y; Dai, XP; Yu, F; Huang, H] School of Computer and Information Engineering, Hunan Agricultural University, Changsha, 410128, China;[Xu, C] College of Computer and Communication, Hunan University, Changsha, 410082, China;[Yu, F] State Key Laboratory of Information Security, Graduate School, Chinese Academy of Sciences, Beijing, 100049, China
作者机构：
[Liao, Gui-ping; Li, Jin-wei] Agricultural Information Institute, Hunan Agricultural University, Changsha, 410128 China;[Wang, Fang] College of Science, Hunan Agricultural University, Changsha, 410128 China;[Liao, Deng-wen] Forestry Department of Hunan Province, Quality Testing and Inspection Centre of Forest Products, Changsha, 410007 China
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：
[Liao, Gui-Ping; Wang, Fang] Agricultural Information Institute, Hunan Agricultural University, Changsha 410128, China;[Li, Zong-Shou] College of Information Science and Engineering, Jishou University, Jishou 416000, China;[Wang, Fang] College of Science, Hunan Agricultural University, Changsha 410128, China
作者机构：
[Li, Jin-wei] Agricultural Information Institute, Hunan Agricultural University, Changsha, China;[Li, Zong-shou] College of Information Science and Engineering, Jishou University, Jishou, China;[Wang, Fang] College of Science, 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.
