检索结果-内蒙古大学图书馆

microarray data analysis on gene and miRNA expression to identify biomarkers in non-small cell lung cancer

BMC CANCER 2020年第1期20卷 1-10页

作者： Jin, Xiang Guan, Yinghui Zhang, Zhen Wang, Hongyue First Hosp Jilin Univ Dept Respirat 1 Xinminda St Changchun 130021 Peoples R China First Hosp Jilin Univ PICU Changchun 130021 Peoples R China First Hosp Jilin Univ Dept Nephrol Changchun 130021 Peoples R China

Background The aim of this study was to gain further investigation of non-small cell lung cancer (NSCLC) tumorigenesis and identify biomarkers for clinical management of patients through comprehensive bioinformatics analysis. Methods miRNA and mRNA microarray datasets were downloaded from GEO (Gene Expression Omnibus) database under the accession number GSE102286 and GSE101929, respectively. Genes and miRNAs with differential expression were identified in NSCLC samples compared with controls, respectively. The interaction between differentially expressed genes (DEGs) and differentially expressed miRNAs (DEmiRs) was predicted, followed by functional enrichment analysis, and construction of miRNA-gene regulatory network, protein-protein interaction (PPI) network, and competing endogenous RNA (ceRNA) network. Through comprehensive bioinformatics analysis, we anticipate to find novel therapeutic targets and biomarkers for NSCLC. Results A total of 123 DEmiRs (5 up- and 118 down-regulated miRNAs) and 924 DEGs (309 up- and 615 down-regulated genes) were identified. These genes and miRNAs were significantly involved in different pathways including adherens junction, relaxin signaling pathway, and axon guidance. Furthermore, hsa-miR-9-5p, has-miR-196a-5p and hsa-miR-31-5p, as well as hsa-miR-1, hsa-miR-218-5p and hsa-miR-135a-5p were shown to have higher degree in the miRNA-gene regulatory network and ceRNA network, respectively. Furthermore, BIRC5 and FGF2, as well as RTKN2 and SLIT3 were hubs in the PPI network and ceRNA network, respectively. Conclusion Several pathways (adherens junction, relaxin signaling pathway, and axon guidance) miRNAs (hsa-miR-9-5p, has-miR-196a-5p, hsa-miR-31-5p, hsa-miR-1, hsa-miR-218-5p and hsa-miR-135a-5p) and genes (BIRC5, FGF2, RTKN2 and SLIT3) may play important roles in the pathogenesis of NSCLC.

关键词： Non-small cell lung cancer Differentially expressed genes miRNA Regulatory network microarray data analysis

来源：评论

学校读者我要写书评

暂无评论

microarray data analysis with Support Vector Machine 3rd

Microarray Data Analysis with Support Vector Machine

引用

3rd International Conference on Intelligent Technologies and Engineering Systems (ICITES)

作者： Du, Si-Hao Jeng, Jin-Tsong Su, Shun-Feng Chang, Sheng-Chieh Natl Taiwan Univ Sci & Technol Dept Elect Engn Taipei Taiwan Natl Formosa Univ Dept Comp Sci & Informat Engn Huwei Township Yunlin Taiwan Natl Chung Shan Inst Sci & Technol Aeronaut Syst Res Div Taichung Taiwan

ISBN: (纸本)9783319173146;9783319173139

microarray data analysis approach has became a widely used tool for disease detection. It uses tens of thousands of genes as input dimension that would be a huge computational problem for data analysis. In this chapter, the proposed approach deals with selection of feature genes and classification of microarray data under support vector machine (SVM) approach. Feature genes can be finding out according to the adjustable epsilon-support vector regression (epsilon-SVR) and then to select high ranked genes after all microarray data. Moreover, multi-class support vector classification (multi-class SVC) and cross-validation methods apply to acquire great prediction classification accuracy and less computing time.

关键词： Support vector machine Support vector regression Multi-class support vector classification Feature genes microarray data analysis

来源：评论

学校读者我要写书评

暂无评论

Fuzzy Clustering for microarray data analysis: A Review

引用

CURRENT BIOINFORMATICS 2011年第4期6卷 427-443页

作者： Liu, Jin Pham, Tuan D. Univ New S Wales Sch Engn & Informat Technol Canberra ACT 2600 Australia China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Jiangshu Peoples R China

microarray technology is capable of providing biomedical and biological researchers with a massive amount of gene expression information to enable rapid significant discoveries in life sciences. microarray data analysis has been developing at a fast pace during the last decade and has become a popular and standard research method for gene expression studies undertaken by genomics research groups worldwide. Many computational tools have been applied to mine this data in order to discover biologically meaningful knowledge. One of the most useful analysis tools is the fuzzy clustering approach which can be modeled in many types of the continuous partitions of data and are well known for its ability to identify co-expressed genes and annotate functions for novel genes. As the computational analysis of microarray data has been developing rapidly, articles surveying its progress of research and developments are periodically needed. In this paper, we review the recent research into microarray data analysis based on fuzzy clustering algorithms and present a newly developed fuzzy clustering technique which, potentially, can be applied to perform microarray data analysis.

关键词： microarray data analysis fuzzy clustering fuzzy C-means fuzzy hyper-prototype clustering

来源：评论

学校读者我要写书评

暂无评论

EMAAS: An extensible grid-based Rich Internet Application for microarray data analysis and management

引用

BMC BIOINFORMATICS 2008年第1期9卷 1-11页

作者： Barton, G. Abbott, J. Chiba, N. Huang, D. W. Huang, Y. Krznaric, M. Mack-Smith, J. Saleem, A. Sherman, B. T. Tiwari, B. Tomlinson, C. Aitman, T. Darlington, J. Game, L. Sternberg, M. J. E. Butcher, S. A. Univ London Imperial Coll Sci Technol & Med Fac Nat Sci Ctr Bioinformat Div Mol Biosci London SW7 2AZ England Univ London Imperial Coll Sci Technol & Med London E Sci Ctr Dept Comp Fac Engn London SW7 2AZ England Univ London Imperial Coll Sci Technol & Med MRC Ctr Clin Sci London W12 0NN England NCI Lab Immunopathogenesis & Bioinformat Clin Serv Program SAIC Frederick Inc Ft Detrick MD 21702 USA

Background: microarray experimentation requires the application of complex analysis methods as well as the use of non-trivial computer technologies to manage the resultant large data sets. This, together with the proliferation of tools and techniques for microarray data analysis, makes it very challenging for a laboratory scientist to keep up-to-date with the latest developments in this field. Our aim was to develop a distributed e-support system for microarray data analysis and management. Results: EMAAS (Extensible microarray analysis System) is a multi-user rich internet application (RIA) providing simple, robust access to up-to-date resources for microarray data storage and analysis, combined with integrated tools to optimise real time user support and training. The system leverages the power of distributed computing to perform microarray analyses, and provides seamless access to resources located at various remote facilities. The EMAAS framework allows users to import microarray data from several sources to an underlying database, to pre-process, quality assess and analyse the data, to perform functional analyses, and to track data analysis steps, all through a single easy to use web portal. This interface offers distance support to users both in the form of video tutorials and via live screen feeds using the web conferencing tool EVO. A number of analysis packages, including R-Bioconductor and Affymetrix Power Tools have been integrated on the server side and are available programmatically through the Postgres-PLR library or on grid compute clusters. Integrated distributed resources include the functional annotation tool DAVID, GeneCards and the microarray data repositories GEO, CELSIUS and MiMiR. EMAAS currently supports analysis of Affymetrix 3' and Exon expression arrays, and the system is extensible to cater for other microarray and transcriptomic platforms. Conclusion: EMAAS enables users to track and perform microarray data management and analysis tasks

关键词： Server Side microarray data analysis Exon Array Single User Interface Grid Cluster

来源：评论

学校读者我要写书评

暂无评论

A novel hybrid feature selection method for microarray data analysis

引用

APPLIED SOFT COMPUTING 2011年第1期11卷 208-213页

作者： Lee, Chien-Pang Leu, Yungho Natl Taiwan Univ Sci & Technol Dept Informat Management Taipei 106 Taiwan

Recently, many methods have been proposed for microarray data analysis. One of the challenges for microarray applications is to select a proper number of the most relevant genes for data analysis. In this paper, we propose a novel hybrid method for feature selection in microarray data analysis. This method first uses a genetic algorithm with dynamic parameter setting (GADP) to generate a number of subsets of genes and to rank the genes according to their occurrence frequencies in the gene subsets. Then, this method uses the chi(2)-test for homogeneity to select a proper number of the top-ranked genes for data analysis. We use the support vector machine (SVM) to verify the efficiency of the selected genes. Six different microarray datasets are used to compare the performance of the GADP method with the existing methods. The experimental results show that the GADP method is better than the existing methods in terms of the number of selected genes and the prediction accuracy. (c) 2009 Elsevier B.V. All rights reserved.

关键词： Feature selection Hybrid method Genetic algorithm chi(2)-Test for homogeneity microarray data analysis

来源：评论

学校读者我要写书评

暂无评论

Algorithmic and complexity issues of three clustering methods in microarray data analysis

引用

ALGORITHMICA 2007年第2期48卷 203-219页

作者： Tan, Jinsong Chua, Kok Seng Zhang, Louxin Zhu, Song Natl Univ Singapore Dept Math Singapore 117543 Singapore Inst High Performance Comp Singapore 117528 Singapore ST Elect Satcom & Sensor Syst Pte Ltd Singapore 609602 Singapore

The complexity, approximation and algorithmic issues of several clustering problems are studied. These non-traditional clustering problems arise from recent studies in microarray data analysis. We prove the following results. (1) Two variants of the Order-Preserving Submatrix problem are NP-hard. There are polynomial-time algorithms for the Order-Preserving Submatrix problem when the condition or gene sets are fixed. (2) Three variants of the Smooth Clustering problem are NP-hard. The Smooth Subset problem is approximable with ratio 0.5, but it cannot be approximable with ratio 0.5 + delta for any delta > 0 unless NP = P. (3) The inferring plaid model problem is NP-hard.

关键词： microarray data analysis NP-hardness approximation polynomial-time algorithm plaid model smooth clustering order-preserving submatrix

来源：评论

学校读者我要写书评

暂无评论

WebArray: an online platform for microarray data analysis

引用

BMC BIOINFORMATICS 2005年第1期6卷 1-6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

Fuzzy mixed-prototype clustering algorithm for microarray data analysis

引用

NEUROCOMPUTING 2018年 276卷 42-54页

作者： Liu, Jin Pham, Tuan D. Yan, Hong Liang, Zhizhen China Univ Min & Technol Sch Comp Sci Xuzhou 221008 Jiangsu Peoples R China Linkoping Univ Dept Biomed Engn S-58183 Linkoping Sweden City Univ Hong Kong Dept Elect Engn Kowloon Hong Kong Peoples R China

Being motivated by combining the advantages of hyperplane-based pattern analysis and fuzzy clustering techniques, we present in this paper a fuzzy mix-prototype (FMP) clustering for microarray data analysis. By integrating spherical and hyper-planar cluster prototypes, the FMP is capable of capturing latent data models with both spherical and non-spherical geometric structures. Our contributions of the paper can be summarized into three folds: first, the objective function of the FMP is formulated. Second, an iterative solution which minimizes the objective function under given constraints is derived. Third, the effectiveness of the proposed FMP is demonstrated through experiments on yeast and leukemia data sets. (c) 2017 Elsevier B.V. All rights reserved.

关键词： FMP microarray data analysis Fuzzy clustering

来源：评论

学校读者我要写书评

暂无评论

SED, a normalization free method for DNA microarray data analysis

引用

BMC BIOINFORMATICS 2004年第1期5卷 1-8页

作者： Wang, HJ Huang, H Oscient Pharmaceut Corp Waltham MA 02453 USA

Background: analysis of DNA microarray data usually begins with a normalization step where intensities of different arrays are adjusted to the same scale so that the intensity levels from different arrays can be compared with one other. Both simple total array intensity-based as well as more complex "local intensity level" dependent normalization methods have been developed, some of which are widely used. Much less developed methods for microarray data analysis include those that bypass the normalization step and therefore yield results that are not confounded by potential normalization errors. Results: Instead of focusing on the raw intensity levels, we developed a new method for microarray data analysis that maps each gene's expression intensity level to a high dimensional space of SEDs ( Signs of Expression Difference), the signs of the expression intensity difference between a given gene and every other gene on the array. Since SED are unchanged under any monotonic transformation of intensity levels, the SED based method is normalization free. When tested on a multi-class tumor classification problem, simple Naive Bayes and Nearest Neighbor methods using the SED approach gave results comparable with normalized intensity-based algorithms. Furthermore, a high percentage of classifiers based on a single gene's SED gave good classification results, suggesting that SED does capture essential information from the intensity levels. Conclusion: The results of testing this new method on multi-class tumor classification problems suggests that the SED-based, normalization-free method of microarray data analysis is feasible and promising.

关键词： Feature Selection Intensity Level Chip Format Near Neighbor microarray data analysis

来源：评论

学校读者我要写书评

暂无评论

Assessing stability of gene selection in microarray data analysis

引用

BMC BIOINFORMATICS 2006年第1期7卷 1-13页

作者： Qiu, X Xiao, YH Gordon, A Yakovlev, A Univ Rochester Dept Biostat & Computat Biol Rochester NY 14642 USA

Background: The number of genes declared differentially expressed is a random variable and its variability can be assessed by resampling techniques. Another important stability indicator is the frequency with which a given gene is selected across subsamples. We have conducted studies to assess stability and some other properties of several gene selection procedures with biological and simulated data. Results: Using resampling techniques we have found that some genes are selected much less frequently (across sub-samples) than other genes with the same adjusted p-values. The extent to which this type of instability manifests itself can be assessed by a method introduced in this paper. The effect of correlation between gene expression levels on the performance of multiple testing procedures is studied by computer simulations. Conclusion: Resampling represents a tool for reducing the set of initially selected genes to those with a sufficiently high selection frequency. Using resampling techniques it is also possible to assess variability of different performance indicators. Stability properties of several multiple testing procedures are described at length in the present paper.

关键词： False Discovery Rate Gene Selection Bonferroni Adjustment microarray data analysis Multiple Testing Procedure

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：