共查询到20条相似文献,搜索用时 328 毫秒
1.
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2.
Background
The analysis of high-throughput screening data sets is an expanding field in bioinformatics. High-throughput screens by RNAi generate large primary data sets which need to be analyzed and annotated to identify relevant phenotypic hits. Large-scale RNAi screens are frequently used to identify novel factors that influence a broad range of cellular processes, including signaling pathway activity, cell proliferation, and host cell infection. Here, we present a web-based application utility for the end-to-end analysis of large cell-based screening experiments by cellHTS2. 相似文献3.
Casper J Albers Ritsert C Jansen Jan Kok Oscar P Kuipers Sacha AFT van Hijum 《BMC bioinformatics》2006,7(1):205-14
Background
Simulation of DNA-microarray data serves at least three purposes: (i) optimizing the design of an intended DNA microarray experiment, (ii) comparing existing pre-processing and processing methods for best analysis of a given DNA microarray experiment, (iii) educating students, lab-workers and other researchers by making them aware of the many factors influencing DNA microarray experiments. 相似文献4.
5.
Background
With the explosion in data generated using microarray technology by different investigators working on similar experiments, it is of interest to combine results across multiple studies. 相似文献6.
Background
Microarray-based comparative genome hybridization experiments generate data that can be mapped onto the genome. These data are interpreted more easily when represented graphically in a genomic context. 相似文献7.
Background
Many high-throughput genomic experiments, such as Synthetic Genetic Array and yeast two-hybrid, use colony growth on solid media as a screen metric. These experiments routinely generate over 100,000 data points, making data analysis a time consuming and painstaking process. Here we describe ScreenMill, a new software suite that automates image analysis and simplifies data review and analysis for high-throughput biological experiments. 相似文献8.
Background
FCI is an R code for analyzing data from real-time PCR experiments. This algorithm estimates standard curve features as well as nucleic acid concentrations and confidence intervals according to Fieller's theorem. 相似文献9.
David M Mutch Alvin Berger Robert Mansourian Andreas Rytz Matthew-Alan Roberts 《BMC bioinformatics》2002,3(1):17-11
Background
The biomedical community is developing new methods of data analysis to more efficiently process the massive data sets produced by microarray experiments. Systematic and global mathematical approaches that can be readily applied to a large number of experimental designs become fundamental to correctly handle the otherwise overwhelming data sets. 相似文献10.
11.
Background
Protein-protein interaction data used in the creation or prediction of molecular networks is usually obtained from large scale or high-throughput experiments. This experimental data is liable to contain a large number of spurious interactions. Hence, there is a need to validate the interactions and filter out the incorrect data before using them in prediction studies. 相似文献12.
Background
In microarray experiments the numbers of replicates are often limited due to factors such as cost, availability of sample or poor hybridization. There are currently few choices for the analysis of a pair of microarrays where N = 1 in each condition. In this paper, we demonstrate the effectiveness of a new algorithm called PINC (PINC is Not Cyber-T) that can analyze Affymetrix microarray experiments. 相似文献13.
Background
The proliferate nature of DNA microarray results have made it necessary to implement a uniform and quick quality control of experimental results to ensure the consistency of data across multiple experiments prior to actual data analysis. 相似文献14.
Matthew E Ritchie Dileepa Diyagama Jody Neilson Ryan van Laar Alexander Dobrovic Andrew Holloway Gordon K Smyth 《BMC bioinformatics》2006,7(1):261-16
Background
Assessment of array quality is an essential step in the analysis of data from microarray experiments. Once detected, less reliable arrays are typically excluded or "filtered" from further analysis to avoid misleading results. 相似文献15.
Johan Vallon-Christersson Nicklas Nordborg Martin Svensson Jari Häkkinen 《BMC bioinformatics》2009,10(1):330-7
Background
Microarray experiments are increasing in size and samples are collected asynchronously over long time. Available data are re-analysed as more samples are hybridized. Systematic use of collected data requires tracking of biomaterials, array information, raw data, and assembly of annotations. To meet the information tracking and data analysis challenges in microarray experiments we reimplemented and improved BASE version 1.2. 相似文献16.
Patrik Rydén Henrik Andersson Mattias Landfors Linda Näslund Blanka Hartmanová Laila Noppa Anders Sjöstedt 《BMC bioinformatics》2006,7(1):300-17
Background
Recently, a large number of methods for the analysis of microarray data have been proposed but there are few comparisons of their relative performances. By using so-called spike-in experiments, it is possible to characterize the analyzed data and thereby enable comparisons of different analysis methods. 相似文献17.
Background
Time-course microarray experiments are being increasingly used to characterize dynamic biological processes. In these experiments, the goal is to identify genes differentially expressed in time-course data, measured between different biological conditions. These differentially expressed genes can reveal the changes in biological process due to the change in condition which is essential to understand differences in dynamics. 相似文献18.
Background
Innovative extensions of (M) ANOVA gain common ground for the analysis of designed metabolomics experiments. ASCA is such a multivariate analysis method; it has successfully estimated effects in megavariate metabolomics data from biological experiments. However, rigorous statistical validation of megavariate effects is still problematic because megavariate extensions of the classical F-test do not exist. 相似文献19.
Background
Biological studies involve a growing number of distinct high-throughput experiments to characterize samples of interest. There is a lack of methods to visualize these different genomic datasets in a versatile manner. In addition, genomic data analysis requires integrated visualization of experimental data along with constantly changing genomic annotation and statistical analyses. 相似文献20.
Sacha?AFT?van Hijum Anne?de Jong Richard?JS?Baerends Harma?A?Karsens Naomi?E?Kramer Rasmus?Larsen Chris?D?den Hengst Casper?J?Albers Jan?Kok Oscar?P?Kuipers