检索结果-内蒙古大学图书馆

A novel biclustering approach with iterative optimization to analyze gene expression data

Advances and Applications in Bioinformatics and Chemistry 2012年第1期5卷 23-59页

作者： Sutheeworapong, Sawannee Ota, Motonori Ohta, Hiroyuki Kinoshita, Kengo Department of Biological Sciences Graduate School of Biosciences and Biotechnology Tokyo Institute of Technology Tokyo Japan Graduate School of Information Sciences Tohoku University Miyagi Japan Institute of Development Aging and Cancer Tohoku University Miyagi Japan Graduate School of Information Sciences Nagoya University Nagoya Japan

Objective: With the dramatic increase in microarray data, biclustering has become a promising tool for gene expression analysis. Biclustering has been proven to be superior over clustering in identifying multifunctional genes and searching for co-expressed genes under a few specific conditions;that is, a subgroup of all conditions. Biclustering based on a genetic algorithm (GA) has shown better performance than greedy algorithms, but the overlap state for biclusters must be treated more systematically. Results: We developed a new biclustering algorithm (binary-iterative genetic algorithm [BIGA]), based on an iterative GA, by introducing a novel, ternary-digit chromosome encoding function. BIGA searches for a set of biclusters by iterative binary divisions that allow the overlap state to be explicitly considered. In addition, the average of the Pearson's correlation coefficient was employed to measure the relationship of genes within a bicluster, instead of the mean square residual, the popular classical index. As compared to the six existing algorithms, BIGA found highly correlated biclusters, with large gene coverage and reasonable gene overlap. The gene ontology (GO) enrichment showed that most of the biclusters are significant, with at least one GO term over represented. Conclusion: BIGA is a powerful tool to analyze large amounts of gene expression data, and will facilitate the elucidation of the underlying functional mechanisms in living organisms. © 2012 Sutheeworapong et al, publisher and licensee Dove Medical Press Ltd.

关键词： Biclustering Genetic algorithm microarray data Pearson's correlation coefficient

来源：评论

学校读者我要写书评

暂无评论

A hybrid algorithm to infer genetic networks

引用

13th International Conference on Neural Informational Processing

作者： Chuang, Cheng-Long Chen, Chung-Ming Shieh, Grace S. Natl Taiwan Univ Inst Biomed Engn Taipei 106 Taiwan Acad Sinica Inst Stat Sci Taipei 115 Taiwan

ISBN: (纸本)3540464816

A pattern recognition approach, based on shape feature extraction, is proposed to infer genetic networks from time course microarray data. The proposed algorithm learns patterns from known genetic interactions, such as RT-PCR confirmed gene pairs, and tunes the parameters using particle swarm optimization algorithm. This work also incorporates a score function to separate significant predictions from non-significant ones. The prediction accuracy of the proposed method applied to data sets in Spellman et al. (1998) is as high as 91%, and true-positive rate and false-negative rate are about 61% and 1%, respectively. Therefore, the proposed algorithm may be useful for inferring genetic interactions.

关键词： particle swarm optimization snake energy model microarray data genetic networks

来源：评论

学校读者我要写书评

暂无评论

A New Method for Identifying Cancer-Related Gene Association Patterns

引用

7th International Conference on Intelligent Computing (ICIC)

作者： Wang, Hong-Qiang Xie, Xin-Ping Li, Ding Chinese Acad Sci Hefei Inst Intelligent Machine Intelligent Comp Lab PO 1130 Hefei 230031 Peoples R China Anhui Univ Architecture Dept Math & Phys Hefei 230022 Peoples R China

ISBN: (纸本)9783642245527;9783642245534

Gene association plays important roles in complex genetic pathology of cancer. However, development of methods for finding cancer-related gene associations is still in its infancy. Based on a biological concept of gene association module (GAM) comprising a center gene and its expression-related genes, this paper proposes a gene association detection model called kernel GAM (kGAM). In the model, we assume that the expression of the center gene can be predicted by the expression-related genes. Based on defining a cost function, a kernel ridge regression algorithm is developed to solve the kGAM model. Finally, to identify a compact GAM for a given center gene, a heuristic search procedure is designed. Experimental results on three publicly available gene expression data sets show the effectiveness and efficiency of the proposed kGAM model in identifying cancer-related gene association patterns.

关键词： microarray data kernels ridge regression gene association

来源：评论

学校读者我要写书评

暂无评论

A Functional Workbench for Anopheles gambiae Micro Array Analysis

A Functional Workbench for <i>Anopheles gambiae</i> Micro Ar...

引用

UKSim-AMSS 7th European Modelling Symposium on Computer Modelling and Simulation (EMS)

作者： Adebiyi, Marion Oghuan, Josiah Fatumo, Segun Adebiyi, Ezekiel Rasgon, Jason Covenant Univ Dept Comp & Informat Sci Ota Nigeria Penn State Univ Dept Entomol Ctr Infect Dis Dynam University Pk PA 16802 USA Penn State Univ Inst Life Sci University Pk PA 16802 USA

ISBN: (纸本)9781479925780

Insecticide resistance, a character inherited that encompasses alteration in one or more of insect's genes is now a major public health challenge combating world efforts on malaria control strategies. Anopheles has developed heavy resistance to pyrethroids, the only World Health Organization (WHO) recommended class for Indoor Residual Spray (IRS) and Long-Lasting Insecticide Treated Nets (LLITNs) through P450 pathways. We used the biochemical network of Anopheles gambiae (henceforth Ag) to deduce its resistance mechanism(s) using two expression data (when Ag is treated with pyrethroid and when controlled). The employed computational techniques are accessible by a robust, multi-faceted and friendly automated graphic user interface (GUI) tagged 'workbench' with JavaFX Scenebuilder. In this work, we introduced a computational platform to determine and also elucidate for the first time resistance mechanism to a commonly used class of insecticide, Pyrethroid. Significantly, our work is the first computational work to identify genes associated or involved in the efflux system in Ag and as a resistance mechanism in the Anopheles.

关键词： Anopheles gambiae biochemical network microarray data resistance mechanism and Features extraction

来源：评论

学校读者我要写书评

暂无评论

Evolutionary Algorithm Based on New Crossover for the Biclustering of Gene Expression data

Evolutionary Algorithm Based on New Crossover for the Biclus...

引用

9th IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB)

作者： Maatouk, Ons Ayadi, Wassim Bouziri, Hend Duval, Beatrice Univ Tunis ISG Tunis LARODEC Lab Tunis Tunisia Univ Angers LERIA F-49045 Angers France Univ Tunis LaTICE Lab ESSTT Tunis Tunisia

ISBN: (纸本)9783319091921;9783319091914

microarray represents a recent multidisciplinary technology. It measures the expression levels of several genes under different biological conditions, which allows to generate multiple data. These data can be analyzed through biclustering method to determinate groups of genes presenting a similar behavior under specific groups of conditions. This paper proposes a new evolutionary algorithm based on a new crossover method, dedicated to the biclustering of gene expression data. This proposed crossover method ensures the creation of new biclusters with better quality. To evaluate its performance, an experimental study was done on real microarray datasets. These experimentations show that our algorithm extracts high quality biclusters with highly correlated genes that are particularly involved in specific ontology structure.

关键词： Biclustering Evolutionary algorithm Crossover method microarray data data mining

来源：评论

学校读者我要写书评

暂无评论

Multi-objective Evolutionary Algorithm for Biclustering in microarrays data

Multi-objective Evolutionary Algorithm for Biclustering in M...

引用

IEEE Congress on Evolutionary Computation (CEC)

作者： Seridi, Khedidja Jourdan, Laetitia Talbi, El-Ghazali INRIA Lille Nord Europe LIFL CNRS F-59650 Villeneuve Dascq France

ISBN: (纸本)9781424478354

microarrays are a powerful tool in studying genes expressions under several conditions. The obtained data need to be analyzed using data mining methods. Biclustering is a data mining method which consists in simultaneous clustering of rows and columns in a data matrix. Using biclustering, we can extract genes that have similar behavior (co-express) under specific conditions. These genes may share identical biological functions. The aim in analyzing gene expression data is the extraction of maximal number of genes and conditions that present similar behavior. The two objectives to be optimized (size and similarity) are conflicting. Therefore, multi-objective optimization is suitable for biclustering. In our work, we combine a well-known multi-objective genetic algorithm (NSGA-II) with a heuristic to solve the biclutering problem. Due to the huge size of the datasets, we use a string of integers as a solution representation where integers represent the indexes of the rows and the columns. Experimental results on real data set show that our approach can find significant biclusters of high quality.

关键词： Biclustering Multi-objective optimization microarray data

来源：评论

学校读者我要写书评

暂无评论

Classifying carcinomas

引用

Genome Biology 2001年第1期2卷 1-2页

作者： Weitzman, Jonathan B.

来源：评论

学校读者我要写书评

暂无评论

Goodness-of-Fit Test for Large Number of Small data Sets

Goodness-of-Fit Test for Large Number of Small Data Sets

引用

作者： Lee, Hyuneui Texas A&M University

学位级别：Ph.D.

A goodness-of-fit (gof) problem, i.e., testing whether observed data come from a specific distribution is one of the important problems in statistics, and various tests for checking distributional assumptions have been suggested. Most tests are for one data set with a large enough sample sizes. However, this research focuses on the gof problem when there are a large number of small data sets. In other words, we assume that the number of data sets p increases to infinity and the sample size of each small data set n is finite. In this dissertation, we will denote p and n as the number of data sets and the sample sizes of each data sets, respectively. Since the primary interest of this dissertation is testing whether every small data set comes from a known parametric family of distributions with different parameters, it is important to choose a gof test invariant to parameters of unknown distribution. Hence, as a basic approach, we suggest applying empirical distribution function (edf) based gof tests to every small data set and then combining P-values to obtain a single test. Two P-value combining methods, moment based tests and smoothing based tests, are suggested and their pros and cons are discussed. Especially, the two moment based tests, Edgington's method and Fisher's method, are compared with respect to Pitman efficiency and asymptotic power. We also find conditions that guarantee that the asymptotic null distribution of moment based tests based on empirical P-values is the same as that based on exact P-values. When the null is a location and scale family, there is no difficulty in applying the suggested test procedures. However, when the null is not a location and scale family, edf-based tests may depend on unknown parameters. To handle such a problem, we suggest using unconditional P-values and this requires an additional step of estimating the distribution of unknown parameters. Several issues related to estimating the distribution of unknown parameters and

关键词： Goodness-of-fit test microarray data Fisher’s method Edgington’s method Smoothing based tests Thesis

来源：评论

学校读者我要写书评

暂无评论

GO for integration of expression data

引用

In Silico Biology 2011年第1-2期11卷 11-17页

作者： Dotan-Cohen, Dikla Moonshine, Dana Natan, Moshe Shemer-Avni, Yonat Melkman, Avraham A. Department of Computer Science Ben-Gurion University Be'er-Sheva 84105 Israel Department of Virology and Molecular Development Soroka University Medical Center Ben-Gurion University Be'er-Sheva Israel

The low reproducibility of differential expression of individual genes in microarray experiments has led to the suggestion that experiments be analyzed in terms of gene characteristics, such as GO categories or pathways, in order to enhance the robustness of the results. An implicit assumption of this approach is that the different experiments in effect randomly sample the genes participating in an active process. We argue that by the same rationale it is possible to perform this higher-level analysis on the aggregation of genes that are differentially-expressed in different expression-based studies, even if the experiments used different platforms. The aggregation increases the reliability of the results, it has the potential for uncovering signals that are liable to escape detection in the individual experiments, and it enables a more thorough mining of the ever more plentiful microarray data. We present here a proof-of-concept study of these ideas, using ten studies describing the changes in expression profiles of human host genes in response to infection by Retroviridae or Herpesviridae viral families. We supply a tool (accessible at ***/∼waytogo) which enables the user to learn about genes and processes of interest in this study. © 2011/2012 - IOS Press and the authors. All rights reserved.

关键词： data integration gene expression Gene-Ontology microarray data

来源：评论

学校读者我要写书评

暂无评论

Multi-omics “upstream analysis” of regulatory genomic regions helps identifying targets against methotrexate resistance of colon cancer

引用

EuPA Open Proteomics 2016年 13卷 1-13页

作者： Kel, Alexander E. Stegmaier, Philip Valeev, Tagir Koschmann, Jeannette Poroikov, Vladimir Kel-Margoulis, Olga V. Wingender, Edgar Institute of Chemical Biology and Fundamental Medicine SBRAS Novosibirsk Russian Federation Biosoft.ru Ltd Novosibirsk Russian Federation Genexplain GmbH Wolfenbüttel D-38302 Germany A.P. Ershov Institute of Informatics Systems SBRAS Novosibirsk Russian Federation Institute of Biomedical Chemistry Moscow Russian Federation Institute of Bioinformatics University Medical Center Göttingen Göttingen D-37077 Germany

We present an “upstream analysis” strategy for causal analysis of multiple “-omics” data. It analyzes promoters using the TRANSFAC database, combines it with an analysis of the upstream signal transduction pathways and identifies master regulators as potential drug targets for a pathological process. We applied this approach to a complex multi-omics data set that contains transcriptomics, proteomics and epigenomics data. We identified the following potential drug targets against induced resistance of cancer cells towards chemotherapy by methotrexate (MTX): TGFalpha, IGFBP7, alpha9-integrin, and the following chemical compounds: zardaverine and divalproex as well as human metabolites such as nicotinamide N-oxide. © 2016 The Author(s)

关键词： ChIP-seq microarray data Pathway analysis Promoter analysis Proteomics data Upstream analysis

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：