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Searching for Glycomics Role in Stem Cell Development

5th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics

作者： Sorathiya, Anil Jucikas, Tadas Piecewicz, Stephanie Sengupta, Shiladitya Lio, Pietro Univ Cambridge Comp Lab 15 JJ Thomson Ave Cambridge CB3 0FD England Univ Cambridge DAMTP Cambridge CB30WA England MIT Cambridge MA 02139 USA

ISBN: (纸本)9783642025037

We present a methodology to analyze zebrafish knock-out experiment replicated tune series microarray data. The knock-out experiment aimed to elucidate the transcriptomal regulators underlying the glycocalyx-regulation of vasculogenesis by performing global gene expression analysis of NDST mutants and wild-type siblings at three distinct tune points during development. Cluster analysis and the construction of a genetic interaction network allows to identify groups of genes acting ill the process of early stage vasculogenesis. We report;the following findings: we found a large number of gene clusters, particularly glycans, during the three developmental steps of the zebrafish organism. In each step;genes connectivity changes according to two different powerlaws. The clusters are highlighted ill such a way;that it is possible to see the dynamics of the interactions through the time points recorded ill tire microarray experiment. Vegf-related genes seem riot to be involved at transcriptomics level, suggesting alternative regulative pathways do exist, to modulate transcriptomal signatures ill developing zebrafish. Our results show that, there are several glycan-related genies which may be involved in early processes such as vasculogenesis.

关键词： Vasculogenesis zebrafish complex network microarray data

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Similarities of ordered gene lists

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Journal of Bioinformatics and Computational Biology 2006年第3期4卷 693-708页

作者： Yang, Xinan Bentink, Stefan Scheid, Stefanie Spang, Rainer Department of Computational Molecular Biology Max Planck Institute for Molecular Genetics 14195 Berlin Ihnestr. 73 Germany State Key Laboratory of Bioelectronics Southeast University 210096 Nanjing China

Motivation: Many applications of microarray technology in clinical cancer studies aim at detecting molecular features for refined diagnosis. In this paper, we follow an opposite rationale: we try to identify common molecular features shared by phenotypically distinct types of cancer using a meta-analysis of several microarray studies. We present a novel algorithm to uncover that two lists of differentially expressed genes are similar, even if these similarities are not apparent to the eye. The method is based on the ordering in the lists. Results: In a meta-analysis of five clinical microarray studies we were able to detect significant similarities in five of the ten possible comparisons of ordered gene lists. We included studies, where not a single gene can be significantly associated to outcome. The detection of significant similarities of gene lists from different microarray studies is a novel and promising approach. It has the potential to improve upon specialized cancer studies by exploring the power of several studies in one single analysis. Our method is complementary to previous methods in that it does not rely on strong effects of differential gene expression in a single study but on consistent ones across multiple studies. © 2006 Imperial College Press.

关键词： Meta-analysis microarray data Ordered gene lists

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Predicting 5-Year Survival Status of Patients with Breast Cancer based on Supervised Wavelet Method

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Osong Public Health and Research Perspectives 2014年第6期5卷 324-332页

作者： Farhadian, Maryam Mahjub, Hossein Poorolajal, Jalal Moghimbeigi, Abbas Mansoorizadeh, Muharram Department of Epidemiology and Biostatistics School of Public Health Hamadan University of Medical Sciences Hamadan Iran Research Center for Health Sciences and Department of Epidemiology and Biostatistics School of Public Health Hamadan University of Medical Sciences Hamadan Iran Modeling of Noncommunicable Diseases Research Center Department of Epidemiology and Biostatistics School of Public Health Hamadan University of Medical Sciences Hamadan Iran Modeling of Noncommunicable Disease Research Center Department of Biostatistics and Epidemiology School of Public Health Hamadan University of Medical Sciences Hamadan Iran Department of Computer Engineering Faculty of Engineering Bu-Ali Sina University Hamadan Iran

Objectives: Classification of breast cancer patients into different risk classes is very important in clinical applications. It is estimated that the advent of high-dimensional gene expression data could improve patient classification. In this study, a new method for transforming the high-dimensional gene expression data in a low-dimensional space based on wavelet transform (WT) is presented. Methods: The proposed method was applied to three publicly available microarray data sets. After dimensionality reduction using supervised wavelet, a predictive support vector machine (SVM) model was built upon the reduced dimensional space. In addition, the proposed method was compared with the supervised principal component analysis (PCA). Results: The performance of supervised wavelet and supervised PCA based on selected genes were better than the signature genes identified in the other studies. Furthermore, the supervised wavelet method generally performed better than the supervised PCA for predicting the 5-year survival status of patients with breast cancer based on microarray data. In addition, the proposed method had a relatively acceptable performance compared with the other studies. Conclusion: The results suggest the possibility of developing a new tool using wavelets for the dimension reduction of microarray data sets in the classification framework. © 2014.

关键词： Breast cancer microarray data Supervised wavelet Support vector machine

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Gene expression data analysis in subtypes of ovarian cancer using covariance analysis

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Journal of Bioinformatics and Computational Biology 2006年第5期4卷 999-1014页

作者： Olman, Victor Hicks, Chindo Wang, Peng Xu, Ying Department of Biochemistry and Molecular Biology Institute of Bioinformatics University of Georgia Athens GA 30602 United States

Many studies have used microarray technology to identify the molecular signatures of human cancer, yet the critical features of these often unmanageably large set of signatures remain elusive. We have investigated co-expression pattern in four subtypes of ovarian cancer from 104 cancer patients using covariance analysis, treating each subtype of ovarian cancer as a distinct disease entity. We sought gene pairs that were transcriptionally co-expressed in one or multiple subtypes of ovarian cancer, establishing a high confidence network of 87 genes interconnected by significantly high co-expression links that were observed in at least two subtypes of ovarian cancer. We have shown that certain groups of co-expressed gene pairs are cancer subtype specific, through demonstrating significant differences in co-expression patterns of gene pairs between subtypes of ovarian cancer. In addition, we identified a set of 24 genes that classified patients into specific cancer subtypes with a misclassification error rate of less than 5%. Our findings illustrate how large public microarray gene expression datasets could be exploited for identification of cancer subtype specific molecular signatures, and how to classify cancer patients into specific subtypes of cancer using gene expression profiles. © 2006 Imperial College Press.

关键词： Gene expression microarray data Ovarian cancer subtypes

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Missing Value Imputation Using Correlation Coefficient 2nd

Missing Value Imputation Using Correlation Coefficient

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2nd International Conference on Computational Intelligence in Pattern Recognition (CIPR)

作者： Manna, Sweta Pati, Soumen Kumar Maulana Abul Kalam Azad Univ Technol Dept Comp Sci & Engn Nadia W Bengal India Maulana Abul Kalam Azad Univ Technol Dept Bioinformat Nadia W Bengal India

ISBN: (纸本)9789811524493;9789811524486

Missing values of microarray dataset are imputed with the help of gene expression sample values. The process by which missing values are calculated is the mean of gene expression sample values and then discretized the sample values. Those discretized values are used to find the similarities between gene expressions with missing value-related genes and genes with no missing values. The gene from without missing values which is most similar of each missing value-related gene is selected, and Pearson's correlation coefficient of the identified gene with all no missing valuerelated genes is calculated. Now, the genes which have higher correlation coefficient with respect to a threshold value are identified. At last, the missing position of the gene is imputed with the mean expression values of the no missing value-related genes which are selected based on correlation coefficient values.

关键词： microarray data Missing value imputation Similarity measurement Correlation coefficient

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Visualization of gene combinations

Visualization of gene combinations

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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

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Biclustering in gene expression data by tendency

Biclustering in gene expression data by tendency

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IEEE Computational Systems Bioinformatics Conference (CSB 2004)

作者： Liu, JZ Yang, J Wang, W Univ N Carolina Dept Comp Sci Chapel Hill NC 27599 USA

ISBN: (纸本)0769521940

The advent of DNA microarray technologies has revolutionized the experimental study of gene expression. Clustering is the most popular approach of analyzing gene expression data and has indeed proven to be successful in many applications. Our work focuses on discovering a subset of genes which exhibit similar expression patterns along a subset of conditions in the gene expression matrix. Specifically, we are looking for the Order Preserving clusters (OP-Cluster), in each of which a subset of genes induce a similar linear ordering along a subset of conditions. The pioneering work of the OPSM model[3], which enforces the strict order shared by the genes in a cluster, is included in our model as a special case. Our model is more robust than OPSM because similarly expressed conditions are allowed to form order equivalent groups and no restriction is placed on the order within a group. Guided by our model, we design and implement a deterministic algorithm, namely OPC-Tree, to discover OP-Clusters. Experimental study on two real datasets demonstrates the effectiveness of the algorithm in the application of tissue classification and cell cycle identification. In addition, a large percentage of OP-Clusters exhibit significant enrichment of one or more function categories, which implies that OP-Clusters indeed carry significant biological relevance.

关键词： gene expression data microarray data biclustering order preserving

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Predictable

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Genome Biology 2001年第1期2卷 1-2页

作者： Weitzman, Jonathan B. Department of Mathematics Robert Morris University Moon PA 15108 United States

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Gene Selection using a High-Dimensional Regression Model with microarrays in Cancer Prognostic Studies

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CANCER INFORMATICS 2012年第11期11卷 29-39页

作者： Kaneko, Shuhei Hirakawa, Akihiro Hamada, Chikuma Tokyo Univ Sci Grad Sch Engn Dept Management Sci Shinjuku Ku 1-3 Kagurazaka Tokyo 1628601 Japan

Mining of gene expression data to identify genes associated with patient survival is an ongoing problem in cancer prognostic studies using microarrays in order to use such genes to achieve more accurate prognoses. The least absolute shrinkage and selection operator (lasso) is often used for gene selection and parameter estimation in high-dimensional microarray data. The lasso shrinks some of the coefficients to zero, and the amount of shrinkage is determined by the tuning parameter, often determined by cross validation. The model determined by this cross validation contains many false positives whose coefficients are actually zero. We propose a method for estimating the false positive rate (FPR) for lasso estimates in a high-dimensional Cox model. We performed a simulation study to examine the precision of the FPR estimate by the proposed method. We applied the proposed method to real data and illustrated the identification of false positive genes.

关键词： cancer prognostic false positive rate gene selection high-dimensional regression microarray data survival analysis

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Error tolerant model for incorporating biological knowledge with expression data in estimating gene networks

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Statistical Methodology 2006年第1期3卷 1-16页

作者： Imoto, Seiya Higuchi, Tomoyuki Goto, Takao Miyano, Satoru Human Genome Center Institute of Medical Science University of Tokyo Minato-ku Tokyo 108-8639 4-6-1 Shirokanedai Japan Institute of Statistical Mathematics Minato-ku Tokyo 106-8569 4-6-7 Minami-Azabu Japan

We propose a novel statistical method for estimating gene networks based on microarray gene expression data together with information from biological knowledge databases. Although a large amount of gene regulation information has already been stored in some biological databases, there are still errors and missing facts due to experimental problems and human errors. Therefore, we cannot blindly use them for understanding gene regulation and a robust procedure with a statistical model for using such database information is required. By using gene expression data, we provide a probabilistic framework of a joint learning model for repairing database information and for estimating a gene network based on dynamic Bayesian networks, simultaneously. To show the effectiveness of the proposed method, we analyze Saccharomyces cerevisiae cell-cycle gene expression data together with KEGG information. © 2005 Elsevier B.V. All rights reserved.

关键词： Bayesian network Biological knowledge Error tolerant model Gene network microarray data

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