共查询到20条相似文献,搜索用时 31 毫秒
1.
Biclustering of gene expression data by non-smooth non-negative matrix factorization 总被引:1,自引:0,他引:1
Pedro Carmona-Saez Roberto D Pascual-Marqui F Tirado Jose M Carazo Alberto Pascual-Montano 《BMC bioinformatics》2006,7(1):78
Background
The extended use of microarray technologies has enabled the generation and accumulation of gene expression datasets that contain expression levels of thousands of genes across tens or hundreds of different experimental conditions. One of the major challenges in the analysis of such datasets is to discover local structures composed by sets of genes that show coherent expression patterns across subsets of experimental conditions. These patterns may provide clues about the main biological processes associated to different physiological states. 相似文献2.
Anup Parikh Eryong Huang Christopher Dinh Blaz Zupan Adam Kuspa Devika Subramanian Gad Shaulsky 《BMC bioinformatics》2010,11(1):163
Background
Identifying candidate genes in genetic networks is important for understanding regulation and biological function. Large gene expression datasets contain relevant information about genetic networks, but mining the data is not a trivial task. Algorithms that infer Bayesian networks from expression data are powerful tools for learning complex genetic networks, since they can incorporate prior knowledge and uncover higher-order dependencies among genes. However, these algorithms are computationally demanding, so novel techniques that allow targeted exploration for discovering new members of known pathways are essential. 相似文献3.
Weijun Luo Michael S Friedman Kerby Shedden Kurt D Hankenson Peter J Woolf 《BMC bioinformatics》2009,10(1):161
Background
Gene set analysis (GSA) is a widely used strategy for gene expression data analysis based on pathway knowledge. GSA focuses on sets of related genes and has established major advantages over individual gene analyses, including greater robustness, sensitivity and biological relevance. However, previous GSA methods have limited usage as they cannot handle datasets of different sample sizes or experimental designs. 相似文献4.
Background
Microarray technology has made it possible to simultaneously measure the expression levels of large numbers of genes in a short time. Gene expression data is information rich; however, extensive data mining is required to identify the patterns that characterize the underlying mechanisms of action. Clustering is an important tool for finding groups of genes with similar expression patterns in microarray data analysis. However, hard clustering methods, which assign each gene exactly to one cluster, are poorly suited to the analysis of microarray datasets because in such datasets the clusters of genes frequently overlap. 相似文献5.
Microarray data analysis: a practical approach for selecting differentially expressed genes 下载免费PDF全文
David M Mutch Alvin Berger Robert Mansourian Andreas Rytz Matthew-Alan Roberts 《Genome biology》2001,2(12):preprint00-29
Background
The biomedical community is rapidly developing new methods of data analysis for microarray experiments, with the goal of establishing new standards to objectively process the massive datasets produced from functional genomic experiments. Each microarray experiment measures thousands of genes simultaneously producing an unprecedented amount of biological information across increasingly numerous experiments; however, in general, only a very small percentage of the genes present on any given array are identified as differentially regulated. The challenge then is to process this information objectively and efficiently in order to obtain knowledge of the biological system under study and by which to compare information gained across multiple experiments. In this context, systematic and objective mathematical approaches, which are simple to apply across a large number of experimental designs, become fundamental to correctly handle the mass of data and to understand the true complexity of the biological systems under study. 相似文献6.
Monica Chagoyen Pedro Carmona-Saez Hagit Shatkay Jose M Carazo Alberto Pascual-Montano 《BMC bioinformatics》2006,7(1):41
Background
Experimental techniques such as DNA microarray, serial analysis of gene expression (SAGE) and mass spectrometry proteomics, among others, are generating large amounts of data related to genes and proteins at different levels. As in any other experimental approach, it is necessary to analyze these data in the context of previously known information about the biological entities under study. The literature is a particularly valuable source of information for experiment validation and interpretation. Therefore, the development of automated text mining tools to assist in such interpretation is one of the main challenges in current bioinformatics research. 相似文献7.
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Background
Clustering the information content of large high-dimensional gene expression datasets has widespread application in "omics" biology. Unfortunately, the underlying structure of these natural datasets is often fuzzy, and the computational identification of data clusters generally requires knowledge about cluster number and geometry. 相似文献10.
Background
Public repositories of microarray data contain an incredible amount of information that is potentially relevant to explore functional relationships among genes by meta-analysis of expression profiles. However, the widespread use of this resource by the scientific community is at the moment limited by the limited availability of effective tools of analysis. We here describe CLOE, a simple cDNA microarray data mining strategy based on meta-analysis of datasets from pairs of species. The method consists in ranking EST probes in the datasets of the two species according to the similarity of their expression profiles with that of two EST probes from orthologous genes, and extracting orthologous EST pairs from a given top interval of the ranked lists. The Gene Ontology annotation of the obtained candidate partners is then analyzed for keywords overrepresentation. 相似文献11.
Background
In microarray data analysis, factors such as data quality, biological variation, and the increasingly multi-layered nature of more complex biological systems complicates the modelling of regulatory networks that can represent and capture the interactions among genes. We believe that the use of multiple datasets derived from related biological systems leads to more robust models. Therefore, we developed a novel framework for modelling regulatory networks that involves training and evaluation on independent datasets. Our approach includes the following steps: (1) ordering the datasets based on their level of noise and informativeness; (2) selection of a Bayesian classifier with an appropriate level of complexity by evaluation of predictive performance on independent data sets; (3) comparing the different gene selections and the influence of increasing the model complexity; (4) functional analysis of the informative genes. 相似文献12.
Background
Large-scale compilation of gene expression microarray datasets across diverse biological phenotypes provided a means of gathering a priori knowledge in the form of identification and annotation of bimodal genes in the human and mouse genomes. These switch-like genes consist of 15% of known human genes, and are enriched with genes coding for extracellular and membrane proteins. It is of interest to determine the prediction potential of bimodal genes for class discovery in large-scale datasets. 相似文献13.
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Background
One of the challenges in the analysis of microarray data is to integrate and compare the selected (e.g., differential) gene lists from multiple experiments for common or unique underlying biological themes. A common way to approach this problem is to extract common genes from these gene lists and then subject these genes to enrichment analysis to reveal the underlying biology. However, the capacity of this approach is largely restricted by the limited number of common genes shared by datasets from multiple experiments, which could be caused by the complexity of the biological system itself. 相似文献15.
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Michael Gormley William Dampier Adam Ertel Bilge Karacali Aydin Tozeren 《BMC bioinformatics》2007,8(1):415
Background
Independently derived expression profiles of the same biological condition often have few genes in common. In this study, we created populations of expression profiles from publicly available microarray datasets of cancer (breast, lymphoma and renal) samples linked to clinical information with an iterative machine learning algorithm. ROC curves were used to assess the prediction error of each profile for classification. We compared the prediction error of profiles correlated with molecular phenotype against profiles correlated with relapse-free status. Prediction error of profiles identified with supervised univariate feature selection algorithms were compared to profiles selected randomly from a) all genes on the microarray platform and b) a list of known disease-related genes (a priori selection). We also determined the relevance of expression profiles on test arrays from independent datasets, measured on either the same or different microarray platforms. 相似文献17.
Fan Mo Xu Hong Feng Gao Lin Du Jun Wang Gilbert S Omenn Biaoyang Lin 《BMC bioinformatics》2008,9(1):537
Background
Alternative splicing is an important gene regulation mechanism. It is estimated that about 74% of multi-exon human genes have alternative splicing. High throughput tandem (MS/MS) mass spectrometry provides valuable information for rapidly identifying potentially novel alternatively-spliced protein products from experimental datasets. However, the ability to identify alternative splicing events through tandem mass spectrometry depends on the database against which the spectra are searched. 相似文献18.
Background
To date, many genomic and pathway-related tools and databases have been developed to analyze microarray data. In published web-based applications to date, however, complex pathways have been displayed with static image files that may not be up-to-date or are time-consuming to rebuild. In addition, gene expression analyses focus on individual probes and genes with little or no consideration of pathways. These approaches reveal little information about pathways that are key to a full understanding of the building blocks of biological systems. Therefore, there is a need to provide useful tools that can generate pathways without manually building images and allow gene expression data to be integrated and analyzed at pathway levels for such experimental organisms as Arabidopsis. 相似文献19.
Paul D Thomas 《BMC bioinformatics》2010,11(1):312
Background
Phylogenetic relationships between genes are not only of theoretical interest: they enable us to learn about human genes through the experimental work on their relatives in numerous model organisms from bacteria to fruit flies and mice. Yet the most commonly used computational algorithms for reconstructing gene trees can be inaccurate for numerous reasons, both algorithmic and biological. Additional information beyond gene sequence data has been shown to improve the accuracy of reconstructions, though at great computational cost. 相似文献20.
Alexander L Richards Peter Holmans Michael C O'Donovan Michael J Owen Lesley Jones 《BMC bioinformatics》2008,9(1):490