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Transcriptomic signatures in breast cancer

MOLECULAR BIOSYSTEMS 2007年第7期3卷 466-472页

作者： Fu, Jianjiang Jeffrey, Stefanie S. Stanford Univ Med Ctr Stanford CA 94305 USA

High throughput DNA microarray technology has been broadly applied to the study of breast cancer to classify molecular subtypes, to predict outcome, survival, response to treatment, and for the identification of novel therapeutic targets. Although results are promising, this technology will not have a full impact on routine clinical practice until there is further standardization of techniques and optimal clinical trial design. Due to substantial disease heterogeneity and the number of genes being analyzed, collaborative, multi-institutional studies are required to accrue enough patients for sufficient statistical power. Newer bioinformatic approaches are being developed to assist with the analysis of this important data.

关键词： GENE-EXPRESSION SIGNATURE NOTTINGHAM PROGNOSTIC INDEX microarray data MOLECULAR CLASSIFICATION ONCOGENIC PATHWAYS HISTOLOGIC GRADE ADJUVANT THERAPY METASTASIS SURVIVAL TUMORS

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Model-based redesign of global transcription regulation

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NUCLEIC ACIDS RESEARCH 2009年第5期37卷 e38-e38页

作者： Carrera, Javier Rodrigo, Guillermo Jaramillo, Alfonso Ecole Polytech CNRS Biochim Lab F-91128 Palaiseau France Univ Politecn Valencia CSIC Inst Biol Mol & Celular Plantas Valencia 46022 Spain Univ Politecn Valencia Inst Aplicac Tecnol Informat & Comunicac Avanzada Valencia 46022 Spain Univ Evry Val Essonne Genopole CNRS Epigenom Project F-91034 Evry France

Synthetic biology aims to the design or redesign of biological systems. In particular, one possible goal could be the rewiring of the transcription regulation network by exchanging the endogenous promoters. To achieve this objective, we have adapted current methods to the inference of a model based on ordinary differential equations that is able to predict the network response after a major change in its topology. Our procedure utilizes microarray data for training. We have experimentally validated our inferred global regulatory model in Escherichia coli by predicting transcriptomic profiles under new perturbations. We have also tested our methodology in silico by providing accurate predictions of the underlying networks from expression data generated with artificial genomes. In addition, we have shown the predictive power of our methodology by obtaining the gene profile in experimental redesigns of the E. coli genome, where rewiring the transcriptional network by means of knockouts of master regulators or by upregulating transcription factors controlled by different promoters. Our approach is compatible with most network inference methods, allowing to explore computationally future genome-wide redesign experiments in synthetic biology.

关键词： .providing profiles microarray data expression data obtaining predictive power knockouts perturbations global transcription

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Supervised redundant feature detection for tumor classification

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BMC MEDICAL GENOMICS 2014年第2-Sup期7卷 S5-S5页

作者： Zeng, Xue-Qiang Li, Guo-Zheng Nanchang Univ Ctr Comp Nanchang 330029 Peoples R China Tongji Univ Dept Control Sci & Engn Shanghai 201804 Peoples R China Tongji Univ Key Lab Embedded Syst & Serv Comp Shanghai 201804 Peoples R China

Background: As a high dimensional problem, analysis of microarray data sets is a challenging task, where many weakly relevant or redundant features affect overall performance of classifiers. Methods: The previous works used redundant feature detection methods to select discriminative compact gene set, which only considered the relationship among features, not the redundancy of classification ability among features. This study propose a novel algorithm named RESI (Redundant fEature Selection depending on Instance), which considers label information in the measure of feature subset redundancy. Results: Experimental results on benchmark data sets show that RESI performs better than the previous state of the-art algorithms on redundant feature selection methods like mRMR. Conclusions: We propose an effective supervised redundant feature detection method for tumor classification.

关键词： Feature selection Redundant feature microarray data Tumor classification

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Testing the prediction error difference between 2 predictors

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BIOSTATISTICS 2009年第3期10卷 550-560页

作者： van de Wiel, Mark A. Berkhof, Johannes van Wieringen, Wessel N. Vrije Univ Amsterdam Med Ctr Dept Epidemiol & Biostat NL-1007 MB Amsterdam Netherlands Vrije Univ Amsterdam Dept Math NL-1081 HV Amsterdam Netherlands

We develop an inference framework for the difference in errors between 2 prediction procedures. The 2 procedures may differ in any aspect and possibly utilize different sets of covariates. We apply training and testing on the same data set, which is accommodated by sample splitting. For each split, both procedures predict the response of the same samples, which results in paired residuals to which a signed-rank test is applied. Multiple splits result in multiple p-values. The median p-value and the mean inverse normal transformed p-value are proposed as summary (test) statistics, for which bounds on the overall type I error rate under a variety of assumptions are proven. A simulation study is performed to check type I error control of the least conservative bound. Moreover, it confirms superior power of our method with respect to a one-split approach. Our inference framework is applied to genomic survival data sets to study 2 issues: compare lasso and ridge regression and decide upon use of both methylation and gene expression markers or the latter only. The framework easily accommodates any prediction paradigm and allows comparing any 2, possibly nonmodel-based, prediction procedures.

关键词： Cross-validation microarray data Model comparison Nonparametric testing

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RAD and the RAD study-annotator: an approach to collection, organization and exchange of all relevant information for high-throughput gene expression studies

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BIOINFORMATICS 2004年第4期20卷 452-459页

作者： Manduchi, E Grant, GR He, H Liu, J Mailman, MD Pizarro, AD Whetzel, PL Stoeckert, CJ Univ Penn Ctr Bioinformat Philadelphia PA 19104 USA

Motivation: Gene expression array technology has become increasingly widespread among researchers who recognize its numerous promises. At the same time, bench biologists and bioinformaticians have come to appreciate increasingly the importance of establishing a collaborative dialog from the onset of a study and of collecting and exchanging detailed information on the many experimental and computational procedures using a structured mechanism. This is crucial for adequate analyses of this kind of data. Results: The RNA Abundance database (RAD;http://***/RAD) provides a comprehensive MIAME-supportive infrastructure for gene expression data management and makes extensive use of ontologies. Specific details on protocols, biomaterials, study designs, etc. are collected through a user-friendly suite of web annotation forms. Software has been developed to generate MAGE-ML documents to enable easy export of studies stored in RAD to any other database accepting data in this format (e.g. ArrayExpress). RAD is part of a more general Genomics Unified Schema (http://***), which includes a richly annotated gene index (http://***), thus providing a platform that integrates genomic and transcriptomic data from multiple organisms. This infrastructure enables a large variety of queries that incorporate visualization and analysis tools and have been tailored to serve the specific needs of projects focusing on particular organisms or biological systems.

关键词： microarray data Standards Ontology

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Simultaneous testing of multiple hypotheses using generalized p-values

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STATISTICS & PROBABILITY LETTERS 2007年第12期77卷 1362-1370页

作者： Tsui, Kam-Wah Tang, Shijie Univ Wisconsin Dept Stat Madison WI 53706 USA

In the context of simultaneously testing many hypotheses, Benjainini and Hochberg [1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Roy. Statist. Soc. Set. B (Methodological) 57, 289-300] propose a procedure that guarantees that the false discovery rate (FDR) will be less than or equal to a specified value. Here, the FDR is the expected value of the ratio of the number of incorrectly rejected hypotheses and the total number of rejected hypotheses. To our knowledge, all of the existing research assumes that it usual p-value is available for each hypothesis. However, in circumstances when nuisance parameters are present, the usual p-values may not be free of the nuisance parameters. We develop a simultaneous testing procedure to control the FDR for the problem of simultaneously testing many Behrens-Fisher problems. Our multiple testing procedure, based on generalized p-values [Tsui, K.-W., Weerahandi, S., 1989. Generalized p-values in significance testing of hypotheses in the presence of nuisance parameters. J. Amer. Statist. Assoc. 84(406), 602-607], is then illustrated with in application to data from a microarray experiment. (c) 2007 Elsevier B.V. All rights reserved.

关键词： Behrens-Fisher problem false discovery rate ratio of two normal means microarray data

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A comparison of bootstrap methods and an adjusted bootstrap approach for estimating the prediction error in microarray classification

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STATISTICS IN MEDICINE 2007年第29期26卷 5320-5334页

作者： Jiang, Wenyu Simon, Richard Concordia Univ Dept Math & Stat Montreal PQ H3G 1M8 Canada NCI NIH Div Canc Treatment & Diag Biometr Res Branch Rockville MD 20852 USA

This paper first provides a critical review on some existing methods for estimating the prediction error in classifying microarray data where the number of genes greatly exceeds the number of specimens. Special attention is given to the bootstrap-related methods. When the sample size n is small, we find that all the reviewed methods suffer from either substantial bias or variability. We introduce a repeated leave-one-out bootstrap (RLOOB) method that predicts for each specimen in the sample using bootstrap learning sets of size ln. We then propose an adjusted bootstrap (ABS) method that fits a learning curve to the RLOOB estimates calculated with different bootstrap learning set sizes. The ABS method is robust across the situations we investigate and provides a slightly conservative estimate for the prediction error. Even with small samples, it does not suffer from large upward bias as the leave-one-out bootstrap and the 0.632+bootstrap, and it does not suffer from large variability as the leave-one-out cross-validation in microarray applications. Copyright (c) 2007 John Wiley & Sons, Ltd.

关键词： bootstrap prediction error class prediction microarray data learning curve feature selection

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INCLUSive: a web portal and service registry for microarray and regulatory sequence analysis

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NUCLEIC ACIDS RESEARCH 2003年第13期31卷 3468-3470页

作者： Coessens, B Thijs, G Aerts, S Marchal, K De Smet, F Engelen, K Glenisson, P Moreau, Y Mathys, J De Moor, B Katholieke Univ Leuven ESAT SCD B-3001 Louvain Belgium

INCLUSive is a suite of algorithms and tools for the analysis of gene expression data and the discovery of cis-regulatory sequence elements. The tools allow normalization, filtering and clustering of microarray data, functional scoring of gene clusters, sequence retrieval, and detection of known and unknown regulatory elements using probabilistic sequence models and Gibbs sampling. All tools are available via different web pages and as web services. The web pages are connected and integrated to reflect a methodology and facilitate complex analysis using different tools. The web services can be invoked using standard SOAP messaging. Example clients are available for download to invoke the services from a remote computer or to be integrated with other applications. All services are catalogued and described in a web service registry. The INCLUSive web portal is available for academic purposes at http://***/inclusive.

关键词： .elements Sampling allow web services acids normalization nucleic inclusive microarray data cis-regulatory web portal service registry regulatory sequence analysis

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A note on the false discovery rate and inconsistent comparisons between experiments

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BIOINFORMATICS 2008年第10期24卷 1225-1228页

作者： Higdon, Roger van Belle, Gerald Kolker, Eugene Seattle Childrens Res Inst Seattle WA 98101 USA Univ Washington Dept Biostat & Environm & Occupat Hlth Sci Seattle WA 98195 USA Univ Washington Dept Med Educ & Biomed Informat Div Biomed Informat Seattle WA 98195 USA

Motivation: The false discovery rate (FDR) has been widely adopted to address the multiple comparisons issue in high-throughput experiments such as microarray gene-expression studies. However, while the FDR is quite useful as an approach to limit false discoveries within a single experiment, like other multiple comparison corrections it may be an inappropriate way to compare results across experiments. This article uses several examples based on gene-expression data to demonstrate the potential misinterpretations that can arise from using FDR to compare across experiments. Researchers should be aware of these pitfalls and wary of using FDR to compare experimental results. FDR should be augmented with other measures such as p-values and expression ratios. It is worth including standard error and variance information for meta-analyses and, if possible, the raw data for re-analyses. This is especially important for high-throughput studies because data are often re-used for different objectives, including comparing common elements across many experiments. No single error rate or data summary may be appropriate for all of the different objectives.

关键词： MULTIPLE-COMPARISON PROCEDURES microarray data HYPOTHESIS CRITERIA

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Identification of Biological Targets of Therapeutic Intervention for Diabetic Nephropathy with Bioinformatics Approach

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EXPERIMENTAL AND CLINICAL ENDOCRINOLOGY & DIABETES 2014年第10期122卷 587-591页

作者： Wu, T. Li, Q. Wu, T. Liu, H. Y. Shandong Univ Hosp 2 Dept Nephrol Jinan 250033 Shandong Peoples R China Shandong Univ Inst Biomed Engn Sch Med Jinan 250033 Shandong Peoples R China Qilu Univ Technol Jinan Shandong Peoples R China

We aimed to discover the potential microRNA (miRNA) targets for diabetic nephropathy (DN) treatment. The microarray data of GSE1009 was downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between DN patients and normal individuals were analyzed using limma package. The significant miRNAs targeting DEGs were collected based on Web-based Gene Set Enrichment Analysis Toolkit (WebGestalt) system. Then we predicted the protein-protein interaction (PPI) pairs regulated by significant miRNAs and constructed the PPI pairs-miRNA network using Cytoscape software. Besides, the significant function modules were explored using Molecular Complex Detection (MCODE) and Biological Networks Gene Ontology (BiNGO) plugin. Total 752 DEGs were obtained, including 318 down-regulated ones and 434 up-regulated ones. There were 10 significant miRNAs, among which miRNA-25 was the most significant. The PPI pairs-miRNA network was established with 103 PPI pairs and 10 miRNAs. Three function modules were obtained, including module A involved with miRNA-29, module B with miRNA-106 and module C with miRNA-124A and miRNA-21B. MiRNA-25, 29, 124 and 21 play key roles in DN progression and these miRNAs may be potential targets for DN treatment.

关键词： diabetic nephropathy microarray data differential expression genes protein pairs microRNAs

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