检索结果-内蒙古大学图书馆

WebArray: an online platform for microarray data analysis

BMC BIOINFORMATICS 2005年第1期6卷 306-306页

作者： Xia, XQ McClelland, M Wang, YP Sidney Kimmel Canc Ctr Dept Canc Genet San Diego CA 92121 USA Sidney Kimmel Canc Ctr Genom Core Facil San Diego CA 92121 USA

Background: Many cutting-edge microarray analysis tools and algorithms, including commonly used limma and affy packages in Bioconductor, need sophisticated knowledge of mathematics, statistics and computer skills for implementation. Commercially available software can provide a user-friendly interface at considerable cost. To facilitate the use of these tools for microarray data analysis on an open platform we developed an online microarray data analysis platform, WebArray, for bench biologists to utilize these tools to explore data from single/dual color microarray experiments. Results: The currently implemented functions were based on limma and affy package from Bioconductor, the spacings LOESS histogram (SPLOSH) method, PCA-assisted normalization method and genome mapping method. WebArray incorporates these packages and provides a user-friendly interface for accessing a wide range of key functions of limma and others, such as spot quality weight, background correction, graphical plotting, normalization, linear modeling, empirical bayes statistical analysis, false discovery rate (FDR) estimation, chromosomal mapping for genome comparison. Conclusion: WebArray offers a convenient platform for bench biologists to access several cutting-edge microarray data analysis tools. The website is freely available at http://***/webarray/. It runs on a Linux server with Apache and MySQL.

关键词： False Discovery Rate microarray data analysis Affy Package Loess Curve Free Open Source Software

来源：评论

学校读者我要写书评

暂无评论

FEATURE SELECTION BY WEIGHTED-SNR FOR CANCER microarray data CLASSIFICATION

引用

INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2009年第12A期5卷 4627-4635页

作者： Hengpraprohm, Supoj Chongstitvatana, Prabhas Nakhon Pathom Rajabhat Univ Fac Sci & Technol Nakhon Pathom 73000 Thailand Chulalongkorn Univ Dept Comp Engn Bangkok 10330 Thailand

Feature selection technique is widely used to improve the data analysis of high dimensional data especially in a classification. task. Cancer microarray data classification task belongs to this category. Many researches studied the feature selection for,microarray data classification, The major problem. is that many feature selection methods must pre-define the number of feature. Unfortunately, the number of feature which is suitable is not known a priori. In this paper, we present a method to weight the value of each feature by SNR score. It is not necessary to pre-define the number of feature. Genetic Programming is employed as a classifier. The experimental results indicate that the proposed method yields good prediction accuracy.

关键词： microarray data analysis Cancer classification Feature selection Signal to noise ratio Genetic programming

来源：评论

学校读者我要写书评

暂无评论

DPBC: Distance Based Possibilistic Biclustering With Application to Gene Ezpression analysis

DPBC: Distance Based Possibilistic Biclustering With Applica...

引用

The 3rd International Conference on Bioinformatics and Biomedical Engineering(iCBBE 2009)(第三届生物信息与生物医学工程国际会议)

作者： Mohamed A.Mahfouz Mohamed A.Ismail Department of Computers and Systems Engineering Faculty of Engineering Alexandria University Alexandria 21544Egypt

Biclustering is a key step in analyzing gene expression data by identifying patterns where subset of genes are co-related based on a subset of conditions. This paper proposes a new distance based possibilistic biclustering algorithm (DPBC), in which the average distances between rows and between columns of the bicluster are minimized and at the same time the size of the bicluster is maximized by computing the zeros of the derivative of appropriate objective function. The proposed algorithm uses the possibilistic clustering paradigm similar to another existing possibilistic biclustering algorithm PBC. Whereas PBC is based on residue our approach is applicable to any accepted definition for distances between pairs of rows or columns. Experimental study on the human dataset and several artificial datasets having different noise levels shows that the DPBC algorithm can offer substantial improvements over the previously proposed algorithms.

关键词： Possibilistic biclustering fuzzy clustering biclustering bidimensional clustering microarray data analysis

来源：评论

学校读者我要写书评

暂无评论

A trend pattern assessment approach to microarray gene expression profiling data analysis

引用

PATTERN RECOGNITION LETTERS 2007年第12期28卷 1472-1482页

作者： Cao, Kajia Zhu, Qiuming Iqbal, Javeed Chan, John W. C. Univ Nebraska Dept Comp Sci Omaha NE 68182 USA Univ Nebraska Med Ctr Dept Pathol & Microbiol Omaha NE 68105 USA

We study the problem of how to assess the reliability of a statistical measurement on data set containing unknown quantity of noises, inconsistencies, and outliers. A practical approach that analyzes the dynamical patterns (trends) of the statistical measurements through a sequential extreme-boundary-points (EBP) weed-out process is explored. We categorize the weed-out trend patterns (WOTP) and examine their relation to the reliability of the measurement. The approach is applied to the processes of extracting genes that are predictive to BCL2 translocations and to clinical survival outcomes of diffuse large B-cell lymphoma (DLBCL) from DNA microarray gene expression profiling data sets. Fisher's Discriminate Criterion (FDC) is used as a statistical measurement in the processes. It is found that the weed-out trend analysis (WOTA) approach is effective for qualitatively assessing the statistics-based measurements in the experimentations conducted. (c) 2007 Elsevier B.V. All rights reserved.

关键词： gene expression profiling microarray data analysis boundary points dynamical patterns trend evaluations fisher's discriminate criterion

来源：评论

学校读者我要写书评

暂无评论

Tilescope: online analysis pipeline for high-density tiling microarray data

引用

GENOME BIOLOGY 2007年第5期8卷 R81-R81页

作者： Zhang, Zhengdong D. Rozowsky, Joel Lam, Hugo Y. K. Du, Jiang Snyder, Michael Gerstein, Mark Yale Univ Dept Mol Biophys & Biochem New Haven CT 06520 USA Yale Univ Interdept Program Computat Biol & Bioinformat New Haven CT 06520 USA Yale Univ Dept Comp Sci New Haven CT 06520 USA Yale Univ Dept Mol Cellular & Dev Biol New Haven CT 06520 USA

We developed Tilescope, a fully integrated data processing pipeline for analyzing high-density tiling-array data (http://***). In a completely automated fashion, Tilescope will normalize signals between channels and across arrays, combine replicate experiments, score each array element, and identify genomic features. The program is designed with a modular, three-tiered architecture, facilitating parallelism, and a graphic user-friendly interface, presenting results in an organized web page, downloadable for further analysis.

关键词： high-density tiling microarray high-density oligonucleotide microarray microarray data analysis

来源：评论

学校读者我要写书评

暂无评论

Direct integration of microarrays for selecting informative genes and phenotype classification

引用

INFORMATION SCIENCES 2008年第1期178卷 88-105页

作者： Yoon, Youngrai Lee, Jongchan Park, Sanghyun Bien, Sangjay Chung, Hyun Cheol Rha, Sun Young Yonsei Univ Dept Comp Sci Seoul 120749 South Korea Yonsei Univ Coll Med Canc Metastasis Res Ctr Dept Internal Med Seoul 120749 South Korea Gachon Univ Med & Sci Dept Informat Technol Inchon South Korea

The ability to provide thousands of gene expression values simultaneously makes microarray data very useful for phenotype classification. A major constraint in phenotype classification is that the number of genes greatly exceeds the number of samples. We overcame this constraint in two ways;we increased the number of samples by integrating independently generated microarrays that had been designed with the same biological objectives, and reduced the number of genes involved in the classification by selecting a small set of informative genes. We were able to maximally use the abundant microarray data that is being stockpiled by thousands of different research groups while improving classification accuracy. Our goal is to implement a feature (gene) selection method that can be applicable to integrated microarrays as well as to build a highly accurate classifier that permits straightforward biological interpretation. In this paper, we propose a two-stage approach. Firstly, we performed a direct integration of individual microarrays by transforming an expression value into a rank value within a sample and identified informative genes by calculating the number of swaps to reach a perfectly split sequence. Secondly, we built a classifier which is a parameter-free ensemble method using only the pre-selected informative genes. By using our classifier that was derived from large, integrated microarray sample datasets, we achieved high accuracy, sensitivity, and specificity in the classification of an independent test dataset. (C) 2007 Elsevier Inc. All rights reserved.

关键词： data mining microarray data analysis microarray data integration microarray data classification Informative gene selection

来源：评论

学校读者我要写书评

暂无评论

APPROXIMATION ALGORITHMS FOR BICLUSTERING PROBLEMS

引用

SIAM JOURNAL ON COMPUTING 2008年第4期38卷 1504-1518页

作者： Wang, Lusheng Lin, Yu Liu, Xiaowen City Univ Hong Kong Dept Comp Sci Kowloon Hong Kong Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China

One of the main goals in the analysis of microarray data is to identify groups of genes and groups of experimental conditions (including environments, individuals, and tissues) that exhibit similar expression patterns. This is the so-called biclustering problem. In this paper, we consider two variations of the biclustering problem: the consensus submatrix problem and the bottleneck submatrix problem. The input of the problems contains an m x n matrix A and integers l and k. The consensus submatrix problem is to find an l x k submatrix with l < m and k < n and a consensus vector such that the sum of distances between the rows in the submatrix and the consensus vector is minimized. The bottleneck submatrix problem is to find an l x k submatrix with l < m and k < n, an integer d and a center vector such that the distance between every row in the submatrix and the vector is at most d and d is minimized. We show that both problems are NP-hard and give randomized approximation algorithms for special cases of the two problems. Using standard techniques, we can derandomize the algorithms to get polynomial time approximation schemes for the two problems. To the best of our knowledge, this is the first time that approximation algorithms with guaranteed ratios are presented for microarray data analysis.

关键词： approximation algorithms computational biology microarray data analysis genes

来源：评论

学校读者我要写书评

暂无评论

SEGS: Search for enriched gene sets in microarray data

引用

JOURNAL OF BIOMEDICAL INFORMATICS 2008年第4期41卷 588-601页

作者： Trajkovski, Igor Lavrac, Nada Tolar, Jakub Jozef Stefan Inst Dept Knowledge Technol Ljubljana 1000 Slovenia Univ Nova Gorica Nova Gorica Slovenia Univ Minnesota Div Hematol Oncol & Blood & Marrow Transplantat Minneapolis MN 55455 USA

Gene Ontology (GO) terms are often used to interpret the results of microarray experiments. The most common approach is to perform Fisher's exact tests to find gene sets annotated by GO terms which are over-represented among the genes declared to be differentially expressed in the analysis of microarray data. Another way is to apply Gene Set Enrichment analysis (GSEA) that uses predefined gene sets and ranks of genes to identify significant biological changes in microarray data sets. However, after correcting for multiple hypotheses testing, few (or no) GO terms may meet the threshold for statistical significance, because the relevant biological differences are small relative to the noise inherent to the microarray technology. In addition to the individual GO terms, we propose testing of gene sets constructed as intersections of GO terms, Kyoto Encyclopedia of Genes and Genomes Orthology (KO) terms, and gene sets constructed by using gene-gene interaction data obtained from the ENTREZ database. Our method finds gene sets that are significantly over-represented among differentially expressed genes which cannot be found by the standard enrichment testing methods applied on individual GO and KO terms, thus improving the enrichment analysis of microarray data. (C) 2007 Elsevier Inc. All rights reserved.

关键词： microarray data analysis ontology gene set enrichment

来源：评论

学校读者我要写书评

暂无评论

ExPlain™: finding upstream drug targets in disease gene regulatory networks

引用

SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2008年第5-6期19卷 481-494页

作者： Kel, A. Voss, N. Valeev, T. Stegmaier, P. Kel-Margoulis, O. Wingender, E. BIOBASE GmbH Wolfenbuttel Germany Inst Chem Biol & Fundamental Med Novosibirsk Russia BoRainbow Inc Novosibirsk Russia Univ Gottingen UKG Dept Bioinformat D-3400 Gottingen Germany

Different signal transduction pathways leading to the activation of transcription factors (TFs) converge at key molecules that master the regulation of many cellular processes. Such crossroads of signalling networks often appear as Achilles Heels causing a disease when not functioning properly. Novel computational tools are needed for analysis of the gene expression data in the context of signal transduction and gene regulatory pathways and for identification of the key nodes in the networks. An integrated computational system, ExPlain (TM) (***) was developed for causal interpretation of gene expression data and identification of key signalling molecules. The system utilizes data from two databases (TRANSFAC (R) and TRANSPATH (R)) and integrates two programs: (1) Composite Module Analyst (CMA) analyses 5'-upstream regions of co-expressed genes and applies a genetic algorithm to reveal composite modules (CMs) consisting of co-occurring single TF binding sites and composite elements;(2) ArrayAnalyzer (TM) is a fast network search engine that analyses signal transduction networks controlling the activities of the corresponding TFs and seeks key molecules responsible for the observed concerted gene activation. ExPlain (TM) system was applied to microarray data on inflammatory bowel diseases (IBD). The results obtained suggest a number of highly interesting biological hypotheses about molecular mechanisms of pathological genetic disregulation.

关键词： microarray data analysis promoter analysis transcription factors composite elements signal transduction drug targets

来源：评论

学校读者我要写书评

暂无评论

Modified fuzzy gap statistic for estimating preferable number of clusters in fuzzy k-means clustering

引用

JOURNAL OF BIOSCIENCE AND BIOENGINEERING 2008年第3期105卷 273-281页

作者： Arima, Chinatsu Hakamada, Kazumi Okamoto, Masahiro Hanai, Taizo Kyushu Univ Grad Sch Syst Life Sci Higashi Ku Fukuoka 8128581 Japan

In clustering methods, the estimation of the optimal number of clusters is significant for subsequent analysis. Without detailed biological information on the genes involved, the evaluation of the number of clusters becomes difficult, and we have to rely on an internal measure that is based on the distribution of the data of the clustering result. The Gap statistic has been proposed as a superior method for estimating the number of clusters in crisp clustering. In this study, we proposed a modified Fuzzy Gap statistic (MFGS) and applied it to fuzzy k-means clustering. For estimating the number of clusters, fuzzy k-means clustering with the MFGS was applied to two artificial data sets with noise and to two experimentally observed gene expression data sets. For the artificial data sets, compared with other internal measures, the MFGS showed a higher performance in terms of robustness against noise for estimating the optimal number of clusters. Moreover, it could be used to estimate the optimal number of clusters in experimental data sets. It was confirmed that the proposed MFGS is a useful method for estimating the number of clusters for microarray data sets.

关键词： clustering validity index fuzzy k-means microarray data analysis estimation of number of clusters

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：