The Experts below are selected from a list of 82851 Experts worldwide ranked by ideXlab platform
Chengqi Zhang - One of the best experts on this subject based on the ideXlab platform.
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PAKDD (2) - Combining support vector machines and the t -statistic for gene selection in DNA Microarray Data Analysis
Advances in Knowledge Discovery and Data Mining, 2010Co-Authors: Tao Yang, Vojislave Kecman, Longbing Cao, Chengqi ZhangAbstract:This paper proposes a new gene selection (or feature selection) method for DNA Microarray Data Analysis In the method, the t-statistic and support vector machines are combined efficiently The resulting gene selection method uses both the Data intrinsic information and learning algorithm performance to measure the relevance of a gene in a DNA Microarray We explain why and how the proposed method works well The experimental results on two benchmarking Microarray Data sets show that the proposed method is competitive with previous methods The proposed method can also be used for other feature selection problems.
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An Agent-Based Hybrid System for Microarray Data Analysis
IEEE Intelligent Systems, 2009Co-Authors: Zili Zhang, Pengyi Yang, Chengqi ZhangAbstract:This article reports our experience in agent-based hybrid construction for Microarray Data Analysis. The contributions are twofold: We demonstrate that agent-based approaches are suitable for building hybrid systems in general, and that a genetic ensemble system is appropriate for Microarray Data Analysis in particular. Created using an agent-based framework, this genetic ensemble system for Microarray Data Analysis excels in both sample classification accuracy and gene selection reproducibility.
Guohui Lin - One of the best experts on this subject based on the ideXlab platform.
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A stable gene selection in Microarray Data Analysis.
BMC bioinformatics, 2006Co-Authors: Kun Juh Yang, Zhipeng Cai, Guohui LinAbstract:Microarray Data Analysis is notorious for involving a huge number of genes compared to a relatively small number of samples. Gene selection is to detect the most significantly differentially expressed genes under different conditions, and it has been a central research focus. In general, a better gene selection method can improve the performance of classification significantly. One of the difficulties in gene selection is that the numbers of samples under different conditions vary a lot. Two novel gene selection methods are proposed in this paper, which are not affected by the unbalanced sample class sizes and do not assume any explicit statistical model on the gene expression values. They were evaluated on eight publicly available Microarray Datasets, using leave-one-out cross-validation and 5-fold cross-validation. The performance is measured by the classification accuracies using the top ranked genes based on the training Datasets. The experimental results showed that the proposed gene selection methods are efficient, effective, and robust in identifying differentially expressed genes. Adopting the existing SVM-based and KNN-based classifiers, the selected genes by our proposed methods in general give more accurate classification results, typically when the sample class sizes in the training Dataset are unbalanced.
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BIBE - A model-free and stable gene selection in Microarray Data Analysis
Fifth IEEE Symposium on Bioinformatics and Bioengineering (BIBE'05), 1Co-Authors: Kun Yang, Zhipeng Cai, Guohui LinAbstract:Microarray Data Analysis is notorious for involving a huge number of genes compared to a relatively small number of samples. Detecting the most significantly differentially expressed genes under different conditions, or gene selection, has been a central focus for researchers. The gene selection problem becomes more difficult when the numbers of samples under different conditions vary significantly, or are unbalanced. A novel model-free and stable gene selection method is proposed in this paper, i.e., the method does not assume any statistical model on the gene expression Data and it is not affected by the unbalanced samples. The method has been evaluated on two publicly available Datasets, the leukemia Dataset and the small round blue cell tumor Dataset, where the experimental results showed that the proposed method is efficient and robust in identifying differentially expressed genes.
Pablo Escobar - One of the best experts on this subject based on the ideXlab platform.
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GEPAS, a web-based tool for Microarray Data Analysis and interpretation
Nucleic Acids Research, 2008Co-Authors: Joaquín Tárraga, Ignacio Medina, José Carbonell, Jaime Huerta-cepas, Pablo Minguez, Eva Alloza, Fatima Al-shahrour, Susana Vegas-azcárate, Stefan Goetz, Pablo EscobarAbstract:Gene Expression Profile Analysis Suite (GEPAS) is one of the most complete and extensively used web-based packages for Microarray Data Analysis. During its more than 5 years of activity it has continuously been updated to keep pace with the state-of-the-art in the changing Microarray Data Analysis arena. GEPAS offers diverse Analysis options that include well established as well as novel algorithms for normalization, gene selection, class prediction, clustering and functional profiling of the experiment. New options for time-course (or dose-response) experiments, Microarray-based class prediction, new clustering methods and new tests for differential expression have been included. The new pipeliner module allows automating the execution of sequential Analysis steps by means of a simple but powerful graphic interface. An extensive re-engineering of GEPAS has been carried out which includes the use of web services and Web 2.0 technology features, a new user interface with persistent sessions and a new extended Database of gene identifiers. GEPAS is nowadays the most quoted web tool in its field and it is extensively used by researchers of many countries and its records indicate an average usage rate of 500 experiments per day. GEPAS, is available at http://www.gepas.org.
Kun Juh Yang - One of the best experts on this subject based on the ideXlab platform.
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A stable gene selection in Microarray Data Analysis.
BMC bioinformatics, 2006Co-Authors: Kun Juh Yang, Zhipeng Cai, Guohui LinAbstract:Microarray Data Analysis is notorious for involving a huge number of genes compared to a relatively small number of samples. Gene selection is to detect the most significantly differentially expressed genes under different conditions, and it has been a central research focus. In general, a better gene selection method can improve the performance of classification significantly. One of the difficulties in gene selection is that the numbers of samples under different conditions vary a lot. Two novel gene selection methods are proposed in this paper, which are not affected by the unbalanced sample class sizes and do not assume any explicit statistical model on the gene expression values. They were evaluated on eight publicly available Microarray Datasets, using leave-one-out cross-validation and 5-fold cross-validation. The performance is measured by the classification accuracies using the top ranked genes based on the training Datasets. The experimental results showed that the proposed gene selection methods are efficient, effective, and robust in identifying differentially expressed genes. Adopting the existing SVM-based and KNN-based classifiers, the selected genes by our proposed methods in general give more accurate classification results, typically when the sample class sizes in the training Dataset are unbalanced.
Hong Zhu - One of the best experts on this subject based on the ideXlab platform.
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ICIC (3) - Feature selection for Microarray Data Analysis using mutual information and rough set theory
Computational Intelligence and Bioinformatics, 2006Co-Authors: Wengang Zhou, Chunguang Zhou, Guixia Liu, Hong Zhu, Xiaoyu ChangAbstract: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.
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AIAI - Feature Selection for Microarray Data Analysis Using Mutual Information and Rough Set Theory
IFIP International Federation for Information Processing, 1Co-Authors: Wengang Zhou, Chunguang Zhou, Guixia Liu, Hong ZhuAbstract: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.