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3rd International Conference on Systems and Informatics (ICSAI)

作者： Jiang, Linying Shi, Xin Yu, Donghai Northeastern Univ Software Coll Shenyang Peoples R China

ISBN: (纸本)9781509055210

The number of genes in microarray data is much larger than the number of available effective samples. Therefore, dealing with a small number of microarray data which are represented by high dimensional features, but of high correlations and strong interferences of redundancy and noise, has become one of the important tasks in gene microarray data extraction and classification. In this paper, a new method is proposed. We use wavelet decomposition to extract gene microarray data, and then use the t-test, ReliefF, Wilcoxon test, and other algorithms to select the data after wavelet transform. Finally, the Borda method is used to merge the sorted values. Three datasets were used in the experiment, namely the leukemia dataset, the prostate dataset, and the lung cancer dataset. Experimental results show that the method proposed in this paper can effectively classify the cancer gene microarray data.

关键词： component microarray data wavelet decomposition feature extraction feature selection rank aggregation

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

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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

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Missing Value Estimation in microarray data Using Coregulation and Similarity of Genes

Missing Value Estimation in Microarray Data Using Coregulati...

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World Congress on Information and Communication Technologies

作者： Amit Paul Jaya Sil Computer Science and Engineering Department St. Thomas College of Engineering and Technology Computer Science and Technology Department Bengal Engineering and Science University

ISBN: (纸本)9781467301275

microarray experiments usually generate multiple missing values in gene expression data sets due to several reasons. In the paper, a robust method has been proposed to estimate the missing values in microarray data using biological knowledge of the genes. Missing values are imputed based on the similarity in their characteristics patterns observed among the co-regulated genes. In this approach, first the microarray data are normalized and then the normalized data is discretized to measure similarity between the genes. The estimation accuracy of the proposed method is compared with the existing K-Nearest Neighbor based method and Pattern Similarity Matching (PSM) considering 4000 genes generated from 192 experiments. The experimental results exhibit that the proposed method outperforms other methods in terms of accuracy.

关键词： Similarity factor Similarity probability Co-regulation Oscillation factor microarray data

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A Novel Approach to Select Important Genes from microarray data

A Novel Approach to Select Important Genes from Microarray D...

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2011 China Control and Decision Conference(2011中国控制与决策会议 CCDC)

作者： Xianchang Wang Lishi Zhang Junfu Du School of Science Dalian Ocean University116023DalianChina

Feature subset selection is a well-known pattern recognition problem, which aims to reduce the number of features used in *** or recognition. This reduction is expected to improve the performance of *** algorithms in terms of speed, accuracy and simplicity. Most existing feature selection investigations are not suitable for microarray data, so this paper focuses on gene selection problem. The main contributions of this paper are that a new feature selection method A-score is introduced and constructed an improved fuzzy Bayesian ***. We evaluate the performance of Ascore using three well-known benchmark data sets: the iris data, the wine data, and the Wisconsin breast cancer data and two microarray data: ALL-AML Leukemia and colon cancer. In general, A-score can *** reduce the number of genes, and perform better than T-score and C-score.

关键词： microarray data Feature selection Important genes A-score

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mDAG: a web-based tool for analyzing microarray data with multiple treatments

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BMC BIOINFORMATICS 2011年第SUPPL 7期12卷 A7-A7页

作者： Phan, Vinhthuy T. Vo, Nam S. Sutter, Thomas R. Univ Memphis Dept Comp Sci Memphis TN 38152 USA Univ Memphis Bioinformat Program Memphis TN 38152 USA Univ Memphis Dept Biol Sci Memphis TN 38152 USA

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A Review on Missing Value Imputation Algorithms for microarray Gene Expression data

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CURRENT BIOINFORMATICS 2014年第1期9卷 18-22页

作者： Moorthy, Kohbalan Mohamad, Mohd Saberi Deris, Safaai Univ Technol Malaysia Fac Comp Artificial Intelligence & Bioinformat Res Grp Skudai 81310 Johor Malaysia

Many bioinformatics analytical tools, especially for cancer classification and prediction, require complete sets of data matrix. Having missing values in gene expression studies significantly influences the interpretation of final data. However, to most analysts' dismay, this has become a common problem and thus, relevant missing value imputation algorithms have to be developed and/or refined to address this matter. This paper intends to present a review of preferred and available missing value imputation methods for the analysis and imputation of missing values in gene expression data. Focus is placed on the abilities of algorithms in performing local or global data correlation to estimate the missing values. Approaches of the algorithms mentioned have been categorized into global approach, local approach, hybrid approach, and knowledge assisted approach. The methods presented are accompanied with suitable performance evaluation. The aim of this review is to highlight possible improvements on existing research techniques, rather than recommending new algorithms with the same functional aim.

关键词： Gene expression analysis gene expression data information recovery microarray data missing value estimation missing value imputation

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Numerical Methods for Genetic Regulatory Network Identification Based on a Variational Approach

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COMPUTATIONAL METHODS IN APPLIED MATHEMATICS 2016年第1期16卷 77-103页

作者： Feng, Xiaobing Yoon, Miun Univ Tennessee Dept Math Knoxville TN 37996 USA

This paper studies differential equation-based mathematical models and their numerical solutions for genetic regulatory network identification. The primary objectives are to design, analyze, and test a general variational framework and numerical methods for seeking its approximate solutions for reverse engineering genetic regulatory networks from microarray datasets. In the proposed variational framework, no structure assumption on the genetic network is presumed, instead, the network is solely determined by the microarray profile of the network components and is identified through a well chosen variational principle which minimizes an energy functional. The variational principle serves not only as a selection criterion to pick up the right solution of the underlying differential equation model but also provides an effective mathematical characterization of the small-world property of genetic regulatory networks which has been observed in lab experiments. Five specific models within the variational framework and efficient numerical methods and algorithms for computing their solutions are proposed and analyzed. Model validations using both synthetic network datasets and subnetwork datasets of Saccharomyces cerevisiae (yeast) and E. coli are performed on all five proposed variational models and a performance comparison versus some existing genetic regulatory network identification methods is also provided.

关键词： Genetic network identification differential equation modeling variational methods matrix norms singular value decomposition quasi-Newton methods microarray data model validation

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Optimal False Discovery Rate Control with Kernel Density Estimation in a microarray Experiment

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2016年第3期45卷 771-780页

作者： Kang, Moonsu Gangneung Wonju Natl Univ Dept Informat Stat Gangneung Si South Korea

Most of current false discovery rate (FDR) procedures in a microarray experiment assume restrictive dependence structures, resulting in being less reliable. FDR controlling procedure under suitable dependence structures based on Poisson distributional approximation is shown. Unlike other procedures, the distribution of false null hypotheses is estimated by using kernel density estimation allowing for dependent structures among the genes. Furthermore, we develop an FDR framework that minimizes the false nondiscovery rate (FNR) with a constraint on the controlled level of the FDR. The performance of the proposed FDR procedure is compared with that of other existing FDR controlling procedures, with an application to the microarray study of simulated data.

关键词： False discovery rate False nondiscovery rate Kernel density estimation microarray data Poisson approximation Primary 62 Secondary H15

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COMPUTERS IN BIOLOGY AND MEDICINE 2015年 64卷 236-245页

作者： Zhang, Li Qian, Liqiang Ding, Chuntao Zhou, Weida Li, Fanzhang Soochow Univ Sch Comp Sci & Technol Suzhou 215006 Peoples R China Soochow Univ Prov Key Lab Comp Informat Proc Technol Suzhou 215006 Peoples R China Collaborat Innovat Ctr Novel Software Technol & I Nanjing 210000 Jiangsu Peoples R China AI Speech Ltd Suzhou 215123 Jiangsu Peoples R China

The family of discriminant neighborhood embedding (DNE) methods is typical graph-based methods for dimension reduction, and has been successfully applied to face recognition. This paper proposes a new variant of DNE, called similarity-balanced discriminant neighborhood embedding (SBDNE) and applies it to cancer classification using gene expression data. By introducing a novel similarity function, SBDNE deals with two data points in the same class and the different classes with different ways. The homogeneous and heterogeneous neighbors are selected according to the new similarity function instead of the Euclidean distance. SBDNE constructs two adjacent graphs, or between-class adjacent graph and within-class adjacent graph, using the new similarity function. According to these two adjacent graphs, we can generate the local between-class scatter and the local within-class scatter, respectively. Thus, SBDNE can maximize the between-class scatter and simultaneously minimize the within-class scatter to find the optimal projection matrix. Experimental results on six microarray datasets show that SBDNE is a promising method for cancer classification. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Discriminant neighborhood embedding Adjacent graph Gene expression data Cancer classification microarray data

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Specific Biomarkers: Detection of Cancer Biomarkers Through High-Throughput Transcriptomics data

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COGNITIVE COMPUTATION 2015年第6期7卷 652-666页

作者： Du, Wei Cao, Zhongbo Wang, Yan Zhou, Fengfeng Pang, Wei Chen, Xin Tian, Yuan Liang, Yanchun Jilin Univ Coll Comp Sci & Technol Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China Univ Georgia Inst Bioinformat Dept Biochem & Mol Biol Athens GA 30602 USA Chinese Acad Sci Shenzhen Inst Adv Technol Key Lab Hlth Informat Shenzhen 518055 Guangdong Peoples R China Univ Aberdeen Sch Nat & Comp Sci Aberdeen AB24 3UE Scotland

Cancer is a systemic disease involving dysregulated biological processes of cell proliferation, metabolism, and apoptosis. It is known that some types of cancer have longer life span, and they are even curable if they are diagnosed and treated properly in the early stage. So it is essential to find biomarkers to detect these cancers in their early stages. With the rapid development of high-throughput microarray and sequencing technologies, many biomarker-based cancer early diagnosis assays are proposed and some are already available in the market. Most of the cancer biomarkers are detected through comparing cancer samples versus normal samples in a certain cancer type, but most of them are not in the comparison against other cancer types. In this research, we propose a novel computational method to comprehensively detect highly accurate cancer biomarkers for different groups of cancer types, with a special emphasis on the detection specificity against the control samples including both those from healthy persons and those from other cancer types. Such biomarkers are called specific biomarkers for a given cancer group, which may be defined as cancers of the same type, cancers with similar survival rates, grade, development stage, or cancers in the same human body systems, etc. The proposed algorithm is extensively evaluated across eight cancer types, and the detection performance shows that the specific biomarkers have reasonable sensitivities and very high specificities. The main contributions of this work are (a) the detection of highly specific biomarkers for eight cancer types and (b) the detection of specific biomarkers for cancers with the similar survival rates. The proposed algorithm may also be used to detect specific biomarkers for cancers of given stages, grades or belonging systems, etc.

关键词： Cancer Specific biomarker microarray data Multiple cancer types Survival rate

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