共查询到20条相似文献,搜索用时 15 毫秒
1.
Eric Bareke Michael Pierre Anthoula Gaigneaux Bertrand De Meulder Sophie Depiereux Naji Habra Eric Depiereux 《BMC bioinformatics》2010,11(1):528
Background
Microarray experiments have become very popular in life science research. However, if such experiments are only considered independently, the possibilities for analysis and interpretation of many life science phenomena are reduced. The accumulation of publicly available data provides biomedical researchers with a valuable opportunity to either discover new phenomena or improve the interpretation and validation of other phenomena that partially understood or well known. This can only be achieved by intelligently exploiting this rich mine of information. 相似文献2.
Vidar Beisvag Frode KR Jünge Hallgeir Bergum Lars Jølsum Stian Lydersen Clara-Cecilie Günther Heri Ramampiaro Mette Langaas Arne K Sandvik Astrid Lægreid 《BMC bioinformatics》2006,7(1):470-13
Background
Modern biology has shifted from "one gene" approaches to methods for genomic-scale analysis like microarray technology, which allow simultaneous measurement of thousands of genes. This has created a need for tools facilitating interpretation of biological data in "batch" mode. However, such tools often leave the investigator with large volumes of apparently unorganized information. To meet this interpretation challenge, gene-set, or cluster testing has become a popular analytical tool. Many gene-set testing methods and software packages are now available, most of which use a variety of statistical tests to assess the genes in a set for biological information. However, the field is still evolving, and there is a great need for "integrated" solutions. 相似文献3.
Sotiris Pavlopoulos Trias Thireou George Kontaxakis Andres Santos 《Biomedical engineering online》2007,6(1):36
Background
Dynamic positron emission tomography studies produce a large amount of image data, from which clinically useful parametric information can be extracted using tracer kinetic methods. Data reduction methods can facilitate the initial interpretation and visual analysis of these large image sequences and at the same time can preserve important information and allow for basic feature characterization. 相似文献4.
Background
Visualization of sequence annotation is a common feature in many bioinformatics tools. For many applications it is desirable to restrict the display of such annotation according to a score cutoff, as biological interpretation can be difficult in the presence of the entire data. Unfortunately, many visualisation solutions are somewhat static in the way they handle such score cutoffs. 相似文献5.
Background
Molecular experiments using multiplex strategies such as cDNA microarrays or proteomic approaches generate large datasets requiring biological interpretation. Text based data mining tools have recently been developed to query large biological datasets of this type of data. PubMatrix is a web-based tool that allows simple text based mining of the NCBI literature search service PubMed using any two lists of keywords terms, resulting in a frequency matrix of term co-occurrence. 相似文献6.
Michael Baitaluk Xufei Qian Shubhada Godbole Alpan Raval Animesh Ray Amarnath Gupta 《BMC bioinformatics》2006,7(1):55-13
Background
The goal of information integration in systems biology is to combine information from a number of databases and data sets, which are obtained from both high and low throughput experiments, under one data management scheme such that the cumulative information provides greater biological insight than is possible with individual information sources considered separately. 相似文献7.
Background
The biological interpretation of large-scale gene expression data is one of the paramount challenges in current bioinformatics. In particular, placing the results in the context of other available functional genomics data, such as existing bio-ontologies, has already provided substantial improvement for detecting and categorizing genes of interest. One common approach is to look for functional annotations that are significantly enriched within a group or cluster of genes, as compared to a reference group. 相似文献8.
9.
Victoria Martin-Requena Antonio Mu?oz-Merida M Gonzalo Claros Oswaldo Trelles 《BMC bioinformatics》2009,10(1):16
Background
Nowadays, microarray gene expression analysis is a widely used technology that scientists handle but whose final interpretation usually requires the participation of a specialist. The need for this participation is due to the requirement of some background in statistics that most users lack or have a very vague notion of. Moreover, programming skills could also be essential to analyse these data. An interactive, easy to use application seems therefore necessary to help researchers to extract full information from data and analyse them in a simple, powerful and confident way. 相似文献10.
Background
DNA microarray technology allows for the measurement of genome-wide expression patterns. Within the resultant mass of data lies the problem of analyzing and presenting information on this genomic scale, and a first step towards the rapid and comprehensive interpretation of this data is gene clustering with respect to the expression patterns. Classifying genes into clusters can lead to interesting biological insights. In this study, we describe an iterative clustering approach to uncover biologically coherent structures from DNA microarray data based on a novel clustering algorithm EP_GOS_Clust. 相似文献11.
Geert Vandeweyer Edwin Reyniers Wim Wuyts Liesbeth Rooms R Frank Kooy 《BMC bioinformatics》2011,12(1):4
Background
Microarray technology allows the analysis of genomic aberrations at an ever increasing resolution, making functional interpretation of these vast amounts of data the main bottleneck in routine implementation of high resolution array platforms, and emphasising the need for a centralised and easy to use CNV data management and interpretation system. 相似文献12.
Peter D Wentzell Tobias K Karakach Sushmita Roy M Juanita Martinez Christopher P Allen Margaret Werner-Washburne 《BMC bioinformatics》2006,7(1):343
Background
Modeling of gene expression data from time course experiments often involves the use of linear models such as those obtained from principal component analysis (PCA), independent component analysis (ICA), or other methods. Such methods do not generally yield factors with a clear biological interpretation. Moreover, implicit assumptions about the measurement errors often limit the application of these methods to log-transformed data, destroying linear structure in the untransformed expression data. 相似文献13.
Background
Gene set enrichment testing has helped bridge the gap from an individual gene to a systems biology interpretation of microarray data. Although gene sets are defined a priori based on biological knowledge, current methods for gene set enrichment testing treat all genes equal. It is well-known that some genes, such as those responsible for housekeeping functions, appear in many pathways, whereas other genes are more specialized and play a unique role in a single pathway. Drawing inspiration from the field of information retrieval, we have developed and present here an approach to incorporate gene appearance frequency (in KEGG pathways) into two current methods, Gene Set Enrichment Analysis (GSEA) and logistic regression-based LRpath framework, to generate more reproducible and biologically meaningful results. 相似文献14.
Jan Hummel Michaela Niemann Stefanie Wienkoop Waltraud Schulze Dirk Steinhauser Joachim Selbig Dirk Walther Wolfram Weckwerth 《BMC bioinformatics》2007,8(1):216
Background
In the last decade, techniques were established for the large scale genome-wide analysis of proteins, RNA, and metabolites, and database solutions have been developed to manage the generated data sets. The Golm Metabolome Database for metabolite data (GMD) represents one such effort to make these data broadly available and to interconnect the different molecular levels of a biological system [1]. As data interpretation in the light of already existing data becomes increasingly important, these initiatives are an essential part of current and future systems biology. 相似文献15.
16.
Background
Genome wide association (GWA) studies are now being widely undertaken aiming to find the link between genetic variations and common diseases. Ideally, a well-powered GWA study will involve the measurement of hundreds of thousands of single nucleotide polymorphisms (SNPs) in thousands of individuals. The sheer volume of data generated by these experiments creates very high analytical demands. There are a number of important steps during the analysis of such data, many of which may present severe bottlenecks. The data need to be imported and reviewed to perform initial quality control (QC) before proceeding to association testing. Evaluation of results may involve further statistical analysis, such as permutation testing, or further QC of associated markers, for example, reviewing raw genotyping intensities. Finally significant associations need to be prioritised using functional and biological interpretation methods, browsing available biological annotation, pathway information and patterns of linkage disequilibrium (LD). 相似文献17.
Background
One of the consequences of the rapid and widespread adoption of high-throughput experimental technologies is an exponential increase of the amount of data produced by genome-wide experiments. Researchers increasingly need to handle very large volumes of heterogeneous data, including both the data generated by their own experiments and the data retrieved from publicly available repositories of genomic knowledge. Integration, exploration, manipulation and interpretation of data and information therefore need to become as automated as possible, since their scale and breadth are, in general, beyond the limits of what individual researchers and the basic data management tools in normal use can handle. This paper describes Genephony, a tool we are developing to address these challenges. 相似文献18.
Paulo C Carvalho Juliana SG Fischer Emily I Chen Gilberto B Domont Maria GC Carvalho Wim M Degrave John R Yates III Valmir C Barbosa 《Proteome science》2009,7(1):6-11
Background
Spectral counting is a shotgun proteomics approach comprising the identification and relative quantitation of thousands of proteins in complex mixtures. However, this strategy generates bewildering amounts of data whose biological interpretation is a challenge. 相似文献19.
Richard Shippy Timothy J Sendera Randall Lockner Chockalingam Palaniappan Tamma Kaysser-Kranich George Watts John Alsobrook 《BMC genomics》2004,5(1):61-15
Background
Despite the widespread use of microarrays, much ambiguity regarding data analysis, interpretation and correlation of the different technologies exists. There is a considerable amount of interest in correlating results obtained between different microarray platforms. To date, only a few cross-platform evaluations have been published and unfortunately, no guidelines have been established on the best methods of making such correlations. To address this issue we conducted a thorough evaluation of two commercial microarray platforms to determine an appropriate methodology for making cross-platform correlations. 相似文献20.