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Particle swarm optimization for gene selection in classifying cancer classes

ARTIFICIAL LIFE AND ROBOTICS 2009年第1期14卷 16-19页

作者： Mohamad, Mohd Omatu, Sigeru Deris, Safaai Yoshioka, Michifumi Osaka Prefecture Univ Grad Sch Engn Dept Comp Sci & Intelligent Syst Sakai Osaka 5998531 Japan Univ Teknol Malaysia Fac Comp Sci & Informat Syst Dept Software Engn Skudai 81310 Johore Malaysia

The application of microarray data for cancer classification has recently gained in popularity. The main problem that needs to be addressed is the selection of a small subset of genes from the thousands of genes in the data that contribute to a disease. This selection process is difficult due to the availability of a small number of samples compared with the huge number of genes, many irrelevant genes, and noisy genes. Therefore, this article proposes an improved binary particle swarm optimization to select a near-optimal (small) subset of informative genes that is relevant for the cancer classification. Experimental results show that the performance of the proposed method is superior to the standard version of particle swarm optimization (PSO) and other previous related work in terms of classification accuracy and the number of selected genes.

关键词： Gene selection Hybrid approach microarray data Particle swarm optimization

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CGHAnalyzer: a stand-alone software package for cancer genome analysis using array-based DNA copy number data

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BIOINFORMATICS 2005年第15期21卷 3308-3311页

作者： Margolin, AA Greshock, J Naylor, TL Mosse, Y Maris, JM Bignell, G Saeed, AI Quackenbush, J Weber, BL Univ Penn Abramson Family Canc Res Inst Philadelphia PA 19104 USA Childrens Hosp Philadelphia Philadelphia PA 19104 USA Wellcome Trust Sanger Inst Hinxton Cambs England Inst Genom Res Rockville MD USA

This synopsis provides an overview of array-based comparative genomic hybridization data display, abstraction and analysis using CGHAnalyzer, a software suite, designed specifically for this purpose. CGHAnalyzer can be used to simultaneously load copy number data from multiple platforms, query and describe large, heterogeneous datasets and export results. Additionally, CGHAnalyzer employs a host of algorithms for microarray analysis that include hierarchical clustering and class differentiation. Availability: CGHAnalyzer, the accompanying manual, documentation and sample data are available for download at http://***. This is a Java-based application built in the framework of the TIGR MeV that can run on Microsoft Windows, Macintosh OSX and a variety of Unix-based platforms. It requires the installation of the free Java Runtime Environment 1.4.1 (or more recent) (http://***). Contact: weberb@***

关键词： microarray data CGH data HYBRIDIZATION GENE VISUALIZATION NEUROBLASTOMA TUMORS

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KEGGanim:: pathway animations for high-throughput data

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BIOINFORMATICS 2008年第4期24卷 588-590页

作者： Adler, Priit Reimand, Jueri Jaenes, Juergen Kolde, Raivo Peterson, Hedi Vilo, Jaak Estonian Bioctr Tartu Estonia Univ Tartu Inst Comp Sci EE-50090 Tartu Estonia QureTec Inc Tartu Estonia

Motivation: Gene expression analysis with microarrays has become one of the most widely used high-throughput methods for gathering genome-wide functional data. Emerging-omics fields such as proteomics and interactomics introduce new information sources. With the rise of systems biology, researchers need to concentrate on entire complex pathways that guide individual genes and related processes. Bioinformatics methods are needed to link the existing knowledge about pathways with the growing amounts of experimental data. Results: We present KEGGanim, a novel web-based tool for visualizing experimental data in biological pathways. KEGGanim produces animations and images of KEGG pathways using public or user uploaded high-throughput data. Pathway members are coloured according to experimental measurements, and animated over experimental conditions. KEGGanim visualization highlights dynamic changes over conditions and allows the user to observe important modules and key genes that influence the pathway. The simple user interface of KEGGanim provides options for filtering genes and experimental conditions. KEGGanim may be used with public or private data for 14 organisms with a large collection of public microarray data readily available. Most common gene and protein identifiers and microarray probesets are accepted for visualization input.

关键词： EXPRESSION PROFILES microarray data PATTERNS TISSUES

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VISDA:: an open-source caBIG™ analytical tool for data clustering and beyond

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BIOINFORMATICS 2007年第15期23卷 2024-2027页

作者： Wang, Jiajing Li, Huai Zhu, Yitan Yousef, Malik Nebozhyn, Michael Showe, Michael Showe, Louise Xuan, Jianhua Clarke, Robert Wang, Yue Virginia Polytech Inst & State Univ Dept Elect & Comp Engn Arlington VA 22203 USA NIA NIH RRB Bioinformat Unit Baltimore MD 21224 USA Wistar Inst Anat & Biol Philadelphia PA 19104 USA Georgetown Univ Lombardi Comprehens Camc Ctr Washington DC 20057 USA Georgetown Univ Dept Oncol Washington DC 20057 USA

VISDA (Visual Statistical data Analyzer) is a caBIG (TM) analytical tool for cluster modeling, visualization and discovery that has met silver-level compatibility under the caBIG initiative. Being statistically principled and visually interfaced, VISDA exploits both hierarchical statistics modeling and human gift for pattern recognition to allow a progressive yet interactive discovery of hidden clusters within high dimensional and complex biomedical datasets. The distinctive features of VISDA are particularly useful for users across the cancer research and broader research communities to analyze complex biological data.

关键词： GENE-EXPRESSION microarray data SOFTWARE PATTERNS

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Software development vis a vis collaboration in interdisciplinary research

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NEUROINFORMATICS 2007年第3期5卷 176-177页

作者： Herskovits, Edward H. Univ Penn Dept Radiol Philadelphia PA 19104 USA

The ability to reanalyze microarray data would otentiate research explaining the molecular bases of embryological development, aging, cancer, and a plethora of other diseases. This potential results from the rapidly growing volume of microarray data, and from the wide range of applications of this technology. It is, therefore, not surprising that one of the primary goals of bioinformatics researchers is the development of software that would allow biologists to share, combine, and reanalyze microarray data. Wan and Pavlidis present an excellent overview of efforts to make the reanalysis of microarray data a reality.

关键词： microarray data microarray Platform Laser Capture Microdissection Molecular Biologist microarray Gene Expression data

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Profile likelihood biclustering

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ELECTRONIC JOURNAL OF STATISTICS 2020年第1期14卷 731-768页

作者： Flynn, Cheryl Perry, Patrick AT&T Labs Res New York NY 10007 USA Oscar Hlth New York NY USA

Biclustering, the process of simultaneously clustering the rows and columns of a data matrix, is a popular and effective tool for finding structure in a high-dimensional dataset. Many biclustering procedures appear to work well in practice, but most do not have associated consistency guarantees. To address this shortcoming, we propose a new biclustering procedure based on profile likelihood. The procedure applies to a broad range of data modalities, including binary, count, and continuous observations. We prove that the procedure recovers the true row and column classes when the dimensions of the data matrix tend to infinity, even if the functional form of the data distribution is misspecified. The procedure requires computing a combinatorial search, which can be expensive in practice. Rather than performing this search directly, we propose a new heuristic optimization procedure based on the Kernighan-Lin heuristic, which has nice computational properties and performs well in simulations. We demonstrate our procedure with applications to congressional voting records, and microarray analysis.

关键词： Biclustering block model profile likelihood congressional voting microarray data

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An Association Analysis Approach to Biclustering 09

An Association Analysis Approach to Biclustering

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15th ACM SIGKDD International Conference on Knowledge Discovery and data Mining

作者： Pandey, Gaurav Atluri, Gowtham Steinbach, Michael Myers, Chad L. Kumar, Vipin Univ Minnesota Dept Comp Sci & Engn Minneapolis MN 55455 USA

ISBN: (纸本)9781605584959

The discovery of biclusters, which denote groups of items that show coherent values across a subset of all the transactions in a data set, is an important type of analysis performed on real-valued data sets in various domains, such as biology. Several algorithms have been proposed to find different types of biclusters in such data sets. However, these algorithms are unable to search the space of all possible biclusters exhaustively. Pattern mining algorithms in association analysis also essentially produce biclusters as their result, since the patterns consist of items that are supported by a subset of all the transactions. However, a major limitation of the numerous techniques developed in association analysis is that they are only able to analyze data sets with binary and/or categorical variables, and their application to real-valued data sets often involves some lossy transformation such as discretization or binarization of the attributes. In this paper, we propose a novel association analysis framework for exhaustively and efficiently mining "range support" patterns from such a data set. On one hand, this framework reduces the loss of information incurred by the binarization- and discretization-based approaches, and on the other, it enables the exhaustive discovery of coherent biclusters. We compared the performance of our framework with two standard biclustering algorithms through the evaluation of the similarity of the cellular functions of the genes constituting the patterns/biclusters derived by these algorithms from microarray data. These experiments show that the real-valued patterns discovered by our framework are better enriched by small biologically interesting functional classes. Also, through specific examples, we demonstrate the ability of the RAP framework to discover functionally enriched patterns that are not found by the commonly used biclustering algorithm ISA. The source code and data sets used in this paper, as well as the supplementary materi

关键词： Real-valued data biclustering association analysis range support microarray data functional modules

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Analysis of Gene Network in MCF-7 Human Breast Cancer Cells

Analysis of Gene Network in MCF-7 Human Breast Cancer Cells

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12th International Conference on Control, Automation and Systems (ICCAS)

作者： Shiraishi, Ryohei Nakakuki, Takashi Kogakuin Univ Dept Mech Syst Engn Tokyo 1638677 Japan Kogakuin Univ Mech Engn Tokyo 1638677 Japan

ISBN: (纸本)9788993215045

Recent technological progress on high-throughput measurements for gene expression such as microarray analysis enables us to collect time-series gene expression data for each of tens of thousands of genes. Although a genomic analysis with those data has identified key genes relating to various diseases, few results on estimation of gene regulatory networks with real microarray data are available so far. Recently, the immediately early response (IER) genes upon epidermal growth factor stimulation in a human breast cancer cell line, MCF-7, have been identified in which time-course microarray data were measured during 90 minutes and 63 IER genes were chosen from tens of thousands of genes by using statistical analysis. In this paper, we estimate the gene regulatory networks among the 63 IER genes. To this end, we apply an estimation method based on a mixed logic dynamical modeling developed in an earlier study to the microarray data. However, the original method is executable for continuous gene expression time-series data whereas the real microarray time-course data have very few time points. In addition, some presetting parameters in the model are critical for a successful result on a network estimation. Then, we add a preprocessing and Monte Carlo-based calculation for the original method.

关键词： Gene network Network estimation Mixed logic dynamical modeling Constrained optimization problem microarray data

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Reduct generation and classification of gene expression data

Reduct generation and classification of gene expression data

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1st International Conference on Hybrid Information Technology

作者： Momin, Bashirahamad F. Mitra, Sushmita Gupta, Rana Datta Walchand Coll Engn Dept Comp Sci & Engn Vishrambag Sangli India Indian Stat Inst Machine Intelligence Unit Kolkata India Jadavpur Univ Dept Comp Engn & Sci Kolkata 700032 W Bengal India

ISBN: (纸本)9780769526744

Identification of gene subsets responsible for discerning between available samples of gene microarray data is an important task in Bioinformatics. Due to the large number of genes in samples, there is an exponentially large search space of solutions. The main challenge is to reduce or remove the redundant genes, without affecting discernibility between objects. Reducts, from rough set theory, correspond to a minimal subset of essential genes. We present an algorithm for generating reducts from gene microarray data. It proceeds by preprocessing gene expression data, discretization of real value attributes into categorical followed by positive region based approach for reduct generation. For comparison, different approaches for reduct generation have also been discussed. Results on benchmark gene expression datasets demonstrate more than 90% reduction of redundant genes.

关键词： rough sets Bioinformatics microarray data reduct soft computing classification

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Infinite Latent Process Decomposition

Infinite Latent Process Decomposition

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IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)

作者： Masada, Tomonari Shibata, Yuichiro Oguri, Kiyoshi Nagasaki Univ Dept Comp & Informat Sci Nagasaki Japan

ISBN: (纸本)9781424483044

This paper presents infinite latent process decomposition (iLPD), a new microarray analysis method, as an extension of latent process decomposition in Our method assumes an infinite number of latent processes. Further, our new collapsed variational Bayesian inference improves the inference proposed in [2] in the treatment of Dirichlet hyperparameters. We also give the results of the comparison experiment.

关键词： microarray data Bayesian nonparametrics

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