检索结果-内蒙古大学图书馆

A DSRPCL-SVM Approach to Informative Gene Analysis

Genomics, Proteomics & Bioinformatics 2008年第2期6卷 83-90页

作者： Wei Xiong Zhibin Cai Jinwen Ma School of Mathematical Sciences and Laboratory of Mathematics and Applied Mathematics （LMAM） PekingUniversity Beijing 100871 China

microarray data based tumor diagnosis is a very interesting topic in bioinformatics. One of the key problems is the discovery and analysis of informative genes of a tumor. Although there are many elaborate approaches to this problem, it is still difficult to select a reasonable set of informative genes for tumor diagnosis only with microarray data. In this paper, we classify the genes expressed through microarray data into a number of clusters via the distance sensitive rival penalized competitive learning （DSRPCL） algorithm and then detect the informative gene cluster or set with the help of support vector machine （SVM）. Moreover, the critical or powerful informative genes can be found through further classifications and detections on the obtained informative gene clusters. It is well demonstrated by experiments on the colon, leukemia, and breast cancer datasets that our proposed DSRPCL-SVM approach leads to a reasonable selection of informative genes for tumor diagnosis.

关键词： microarray data informative gene selection clustering analysis DSRPCL tumordiagnosis

来源：评论

学校读者我要写书评

暂无评论

KEGGanim:: pathway animations for high-throughput data

引用

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

来源：评论

学校读者我要写书评

暂无评论

Gene regulatory network reconstruction and pathway inference from high throughput gene expression data

Gene regulatory network reconstruction and pathway inference...

引用

作者： Luo, Weijun University of Michigan

学位级别：Ph.D.

Two basic motivating questions in biomedical research are: What genes regulate what other genes? What genes or groups of genes regulate a specific phenotype? Gene regulatory network (GRN) reconstruction and pathway inference are the two computational strategies addressing these two questions respectively. GRN reconstruction is to infer the components and topology of an unknown pathway, while pathway inference is to infer association between known pathways and a phenotype. This thesis focuses on gene regulatory network reconstruction and pathway inference from high throughput biological data. In the first part of this work, I developed a novel method, MI3, for de novo GRN reconstruction using continuous three-way mutual information. MI3 addresses three major issues in previous probabilistic methods simultaneously: (1) to handle continuous variables, (2) to detect high order relationships, (3) to differentiate causal vs. confounding relationships. MI3 consistently and significantly outperformed frequently used control methods and faithfully capture mechanistic relationships from gene expression data. In the second part of this work, I proposed another novel method, GAGE, Generally Applicable Gene Set Enrichment for pathway inference. I successfully apply GAGE to multiple microarray data sets with different sample sizes, experimental designs and profiling techniques. GAGE shows significantly better performance when compared to two other commonly used GSA methods of GSEA and PAGE. GAGE reveals novel and relevant regulatory mechanisms from both published and previously unpublished microarray studies. In the third part of this work, we conducted a microarray study on transcriptional programs during BMP6 induced osteoblast differentiation and mineralization, and applied GAGE to recover the regulatory pathways and transcriptional signaling networks in the process. I not only showed which pathways or gene sets are significant, but also when and how they are involved in the o

关键词： Gene Regulatory Network Pathway Inference microarray data BMP Induced Osteoblast Differentiation Systems Biology Thesis

来源：评论

学校读者我要写书评

暂无评论

Information Gain with Chaotic Genetic Algorithm for Gene Selection and Classification Problem

Information Gain with Chaotic Genetic Algorithm for Gene Sel...

引用

IEEE International Conference on System, Man, and Cybernetic

作者： Yang, Cheng-San Chuang, Li-Yeh Li, Jung-Chike Yang, Cheng-Hong Natl Cheng Kung Univ Inst Biomed Engn Tainan 70101 Taiwan I Shou Univ Dept Chem Engn Kaohsiung Taiwan Natl Kaohsiung Univ Appl Sci Dept Elect Engn Kaohsiung Taiwan

ISBN: (纸本)9781424423835

For microarray data classification problem, selecting relevant genes from microarray data pose a formidable challenge to researchers due to the high-dimensionality of features, multi-class categories being involved and the usually small sample size. In order to correctly analyze microarray data, the goal of feature (gene) selection is to select those subsets of differentially expressed genes that are potentially relevant for distinguishing the sample classes. In this paper, information gain and chaotic genetic algorithm are proposed to select the relevant genes, and a K-nearest neighbor with the leave-one-out cross-validation method serves as a classifier. Chaotic genetic algorithm is modified by using the chaotic mutation operator to increase the population diversity. The experimental results show that the proposed method not only effectively reduced the number of gene expression levels, but also achieved lower classification error rates.

关键词： microarray data feature selection information gain chaotic genetic algorithm K-nearest neighbor

来源：评论

学校读者我要写书评

暂无评论

Proportionate Feature Selection - A Pre-Processing Step for Clustering

Proportionate Feature Selection - A Pre-Processing Step for ...

引用

IEEE International Conference on System, Man, and Cybernetic

作者： Sekhon, Jashanjot Singh Gopalkrishnan, Vivekanand Keong, Ng Wee Nanyang Technol Univ Singapore 639798 Singapore

ISBN: (纸本)9781424423835

Accuracy and efficiency of clustering algorithms depend greatly on the input data. Thus, removing unimportant features from the dataset can help us form better dusters in lesser time. These unimportant features may be those that are redundant, or affected by noise, etc. We also need to consider the fact that the features we finally choose, should represent the original dataset in the best possible way. In other words, the underlying structure of the original dataset should be the same as that of the dataset that contains only the selected features. In this paper we propose a technique that selects a subset of features that best represent the entire dataset. This technique is based on two measures - Distance Measure and Similarity Measure. We first group the similar features and then select a proportionate number of features from each group. We perform experiments on a gate expression microarray dataset, and our experimental results show that using our technique as a pre-processing step significantly increases the quality of dusters generated by the underlying K-means algorithm. We also demonstrate that our approach is better than other contemporary pre-processing filters.

关键词： Feature Selection Clustering Distance Measure Similarity Measure Proportionate Number of Genes microarray data

来源：评论

学校读者我要写书评

暂无评论

Testing differential expression in nonoverlapping gene pairs: A new perspective for the empirical bayes method

引用

Journal of Bioinformatics and Computational Biology 2008年第2期6卷 301-316页

作者： Klebanov, Lev Qiu, Xing Yakovlev, Andrei Department of Probability and Statistics Charles University Praha-8 CZ-18675 Sokolovska 83 Czech Republic Department of Biostatistics and Computational Biology University of Rochester Rochester NY 14642 601 Elmwood Avenue United States

The currently practiced methods of significance testing in microarray gene expression profiling are highly unstable and tend to be very low in power. These undesirable properties are due to the nature of multiple testing procedures, as well as extremely strong and long-ranged correlations between gene expression levels. In an earlier publication, we identified a special structure in gene expression data that produces a sequence of weakly dependent random variables. This structure, termed the δ-sequence, lies at the heart of a new methodology for selecting differentially expressed genes in nonoverlapping gene pairs. The proposed method has two distinct advantages: (1) it leads to dramatic gains in terms of the mean numbers of true and false discoveries, and in the stability of the results of testing;and (2) its outcomes are entirely free from the log-additive array-specific technical noise. We demonstrate the usefulness of this approach in conjunction with the nonparametric empirical Bayes method. The proposed modification of the empirical Bayes method leads to significant improvements in its performance. The new paradigm arising from the existence of the δ-sequence in biological data offers considerable scope for future developments in this area of methodological research. © 2008 Imperial College Press.

关键词： Correlation Differential expression Error rates Gene pairs microarray data

来源：评论

学校读者我要写书评

暂无评论

Visualization of gene combinations

Visualization of gene combinations

引用

12th International Conference Information Visualisation 2008

作者： Tominski, Christian Schumann, Heidrun Univ Rostock Inst Comp Sci D-2500 Rostock 1 Germany

ISBN: (纸本)9780769532684

Advances in the field of microarray technology have attracted a lot of attention in recent years. More and more biological experiments are conducted based on microarrays. The challenge researchers face today is to analyze and understand the collected data. We present a visual approach to support understanding microarray data. In contrast to other visualization techniques, which represent expression of genes, we go one step further and make a switch to combinations of genes. Gene combinations bear more information, and hence, can lead to new hypotheses about the data. However the increased amount of information imposes several challenges to an interactive visualization approach. We propose analytical and visual methods to deal with these challenges.

关键词： information visualization visual analysis microarray data

来源：评论

学校读者我要写书评

暂无评论

EzArray: A web-based highly automated Affymetrix expression array data management and analysis system

引用

BMC BIOINFORMATICS 2008年第1期9卷 46-46页

作者： Zhu, Yuerong Zhu, Yuelin Xu, Wei BioInfoRx Inc Middleton WI 53562 USA Georgetown Univ Dept Oncol Washington DC 20057 USA Univ Wisconsin Dept Oncol Madison WI 53706 USA

Background: Though microarray experiments are very popular in life science research, managing and analyzing microarray data are still challenging tasks for many biologists. Most microarray programs require users to have sophisticated knowledge of mathematics, statistics and computer skills for usage. With accumulating microarray data deposited in public databases, easy-to-use programs to re-analyze previously published microarray data are in high demand. Results: EzArray is a web-based Affymetrix expression array data management and analysis system for researchers who need to organize microarray data efficiently and get data analyzed instantly. EzArray organizes microarray data into projects that can be analyzed online with predefined or custom procedures. EzArray performs data preprocessing and detection of differentially expressed genes with statistical methods. All analysis procedures are optimized and highly automated so that even novice users with limited pre-knowledge of microarray data analysis can complete initial analysis quickly. Since all input files, analysis parameters, and executed scripts can be downloaded, EzArray provides maximum reproducibility for each analysis. In addition, EzArray integrates with Gene Expression Omnibus (GEO) and allows instantaneous re-analysis of published array data. Conclusion: EzArray is a novel Affymetrix expression array data analysis and sharing system. EzArray provides easy-to-use tools for re-analyzing published microarray data and will help both novice and experienced users perform initial analysis of their microarray data from the location of data storage. We believe EzArray will be a useful system for facilities with microarray services and laboratories with multiple members involved in microarray data analysis. EzArray is freely available from http://***/.

关键词： microarray data Gene Expression Omnibus microarray data Analysis Bioconductor Package Novice User

来源：评论

学校读者我要写书评

暂无评论

Multivariate hierarchical Bayesian model for differential gene expression analysis in microarray experiments

引用

BMC BIOINFORMATICS 2008年第s1期9卷 S9-S9页

作者： Zhao, Hongya Chan, Kwok-Leung Cheng, Lee-Ming Yan, Hong City Univ Hong Kong Dept Elect Engn Kowloon Hong Kong Peoples R China Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia

Background: Identification of differentially expressed genes is a typical objective when analyzing gene expression data. Recently, Bayesian hierarchical models have become increasingly popular to solve this type of problems. These models show good performance in accommodating noise, variability and low replication of microarray data. However, the correlation between different fluorescent signals measured from a gene spot is ignored, which can diversely affect the data analysis step. In fact, the intensities of the two signals are significantly correlated across samples. The larger the log-transformed intensities are, the smaller the correlation is. Results: Motivated by the complicated error relations in microarray data, we propose a multivariate hierarchical Bayesian framework for data analysis in the replicated microarray experiments. Gene expression data are modelled by a multivariate normal distribution, parameterized by the corresponding mean vectors and covariance matrixes with a conjugate prior distribution. Within the Bayesian framework, a generalized likelihood ratio test (GLRT) is also developed to infer the gene expression patterns. Simulation studies show that the proposed approach presents better operating characteristics and lower false discovery rate (FDR) than existing methods, especially when the correlation coefficient is large. The approach is illustrated with two examples of microarray analysis. The proposed method successfully detects significant genes closely related to the experimental states, which are verified by the biological information. Conclusions: The multivariate Bayesian model, compatible with the dependence between mean and variance in the univariate Bayesian model, relaxes the constant coefficient of variation assumption between measurements by adding a covariance structure. This model improves the identification of differentially expressed genes significantly since the Bayesian model fit well with the microarray data.

关键词： False Discovery Rate microarray data Positive Predict Value Negative Predict Value Bayesian Model

来源：评论

学校读者我要写书评

暂无评论

Application of microarray outlier detection methodology to psychiatric research

引用

BMC PSYCHIATRY 2008年第1期8卷 29-29页

作者： Ernst, Carl Bureau, Alexandre Turecki, Gustavo McGill Univ McGill Grp Suicide Studies Montreal PQ Canada Univ Laval Robert Giffard Ctr Rech Laval PQ Canada Univ Laval Dept Social & Prevent Med Quebec City PQ G1K 7P4 Canada Douglas Hosp Res Ctr Montreal PQ H4H 1R3 Canada

Background: Most microarray data processing methods negate extreme expression values or alter them so that they do not lie outside the mean level of variation of the system. While microarrays generate a substantial amount of false positive and spurious results, some of the extreme expression values may be valid and could represent true biological findings. Methods: We propose a simple method to screen brain microarray data to detect individual differences across a psychiatric sample set. We demonstrate in two different samples how this method can be applied. Results: This method targets high-throughput technology to psychiatric research on a subject-specific basis. Conclusion: Assessing microarray data for both mean group effects and individual effects can lead to more robust findings in psychiatric genetics.

关键词： microarray data High Standard Deviation Spinocerebellar Ataxia Multiple Brain Region Suicide Completer

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：