排序方式: 共有149条查询结果,搜索用时 15 毫秒
81.
Gerard Giraud Holger Schulze Till T. Bachmann Colin J. Campbell Andrew R. Mount Peter Ghazal Mizanur R. Khondoker Alan J. Ross Stuart W. J. Ember Ilenia Ciani Chaker Tlili Anthony J. Walton Jonathan G. Terry Jason Crain 《International journal of molecular sciences》2009,10(4):1930-1941
Quantum dot (QD) labeling combined with fluorescence lifetime imaging microscopy is proposed as a powerful transduction technique for the detection of DNA hybridization events. Fluorescence lifetime analysis of DNA microarray spots of hybridized QD labeled target indicated a characteristic lifetime value of 18.8 ns, compared to 13.3 ns obtained for spots of free QD solution, revealing that QD labels are sensitive to the spot microenvironment. Additionally, time gated detection was shown to improve the microarray image contrast ratio by 1.8, achieving femtomolar target sensitivity. Finally, lifetime multiplexing based on Qdot525 and Alexa430 was demonstrated using a single excitation-detection readout channel. 相似文献
82.
Zexuan Zhu Author Vitae Yew-Soon Ong Author Vitae Manoranjan Dash Author Vitae 《Pattern recognition》2007,40(11):3236-3248
Microarray technologies enable quantitative simultaneous monitoring of expression levels for thousands of genes under various experimental conditions. This new technology has provided a new way of biological classification on a genome-wide scale. However, predictive accuracy is affected by the presence of thousands of genes many of which are unnecessary from the classification point of view. So, a key issue of microarray data classification is to identify the smallest possible set of genes that can achieve good predictive accuracy. In this study, we propose a novel Markov blanket-embedded genetic algorithm (MBEGA) for gene selection problem. In particular, the embedded Markov blanket-based memetic operators add or delete features (or genes) from a genetic algorithm (GA) solution so as to quickly improve the solution and fine-tune the search. Empirical results on synthetic and microarray benchmark datasets suggest that MBEGA is effective and efficient in eliminating irrelevant and redundant features based on both Markov blanket and predictive power in classifier model. A detailed comparative study with other methods from each of filter, wrapper, and standard GA shows that MBEGA gives a best compromise among all four evaluation criteria, i.e., classification accuracy, number of selected genes, computational cost, and robustness. 相似文献
83.
Lipid Metabolism is Closely Associated with Normal Testicular Growth Based on Global Transcriptome Profiles in Normal and Underdeveloped Testis of Obese Zucker (<Emphasis Type="Italic">fa/fa</Emphasis>) Rats 下载免费PDF全文
Jutika Datar Alemu Regassa Woo-Kyun Kim Carla G. Taylor Peter Zahradka Miyoung Suh 《Lipids》2017,52(11):951-960
84.
The purpose of normalization in microarray data analysis is to minimize systematic variations in the measured gene expression
levels of two co-hybridized mRNA samples so that biological differences can be more easily distinguished. The most commonly
and widely used normalization procedure for spotted arrays is probably the intensity dependent and print-tip LOWESS normalization.
It is well known that the choices of different parameter values greatly affect the quality of the normalization results, and
thus poor quality of the normalization results could be due to the arbitrary choice of the smoothing parameters for LOWESS
normalization. In many normalization studies, however, LOWESS has been simply used without rigorous consideration of the parameters.
In this article, we propose a bootstrap method to find the optimal window width in print-tip normalization by applying the
cross validation technique. We also compare through simulation studies the normalization results by using the proposed method
with those by fixing the window width. 相似文献
85.
Han-Ming Wu 《Computational statistics & data analysis》2011,55(5):1969-1979
Unsupervised clustering methods such as K-means, hierarchical clustering and fuzzy c-means have been widely applied to the analysis of gene expression data to identify biologically relevant groups of genes. Recent studies have suggested that the incorporation of biological information into validation methods to assess the quality of clustering results might be useful in facilitating biological and biomedical knowledge discoveries. In this study, we generalize two bio-validity indices, the biological homogeneity index and the biological stability index, to quantify the abilities of soft clustering algorithms such as fuzzy c-means and model-based clustering. The results of an evaluation of several existing soft clustering algorithms using simulated and real data sets indicate that the soft versions of the indices provide both better precision and better accuracy than the classical ones. The significance of the proposed indices is also discussed. 相似文献
86.
A. Pedro Duarte Silva 《Computational statistics & data analysis》2011,55(11):2975-2990
A class of linear classification rules, specifically designed for high-dimensional problems, is proposed. The new rules are based on Gaussian factor models and are able to incorporate successfully the information contained in the sample correlations. Asymptotic results, that allow the number of variables to grow faster than the number of observations, demonstrate that the worst possible expected error rate of the proposed rules converges to the error of the optimal Bayes rule when the postulated model is true, and to a slightly larger constant when this model is a reasonable approximation to the data generating process. Numerical comparisons suggest that, when combined with appropriate variable selection strategies, rules derived from one-factor models perform comparably, or better, than the most successful extant alternatives under the conditions they were designed for. The proposed methods are implemented as an R package named HiDimDA, available from the CRAN repository. 相似文献
87.
Classification analysis of microarray data is widely used to reveal biological features and to diagnose various diseases, including cancers. Most existing approaches improve the performance of learning models by removing most irrelevant and redundant genes from the data. They select the marker genes which are expressed differently in normal and tumor tissues. These techniques ignore the importance of the complex functional-dependencies between genes. In this paper, we propose a new method for cancer classification which uses distinguished variations of gene–gene correlation in two sample groups. The cancer specific genetic network composed of these gene pairs contains many literature-curated prostate cancer genes, and we were successful in identifying new candidate prostate cancer genes inferred by them. Furthermore, this method achieved a high accuracy with a small number of genes in cancer classification. 相似文献
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89.
Dong Hyun?JeongEmail author Alireza?Darvish Kayvan?Najarian Jing?Yang William?Ribarsky 《The Visual computer》2008,24(12):1053-1066
Estimating dynamic regulatory pathways using DNA microarray time-series can provide invaluable information about the dynamic
interactions among genes and result in new methods of rational drug design. Even though several purely computational methods
have been introduced for DNA pathway analysis, most of these techniques do not provide a fully interactive method to explore
and analyze these dynamic interactions in detail, which is necessary to obtain a full understanding. In this paper, we present
a unified modeling and visual approach focusing on visual analysis of gene regulatory pathways over time. As a preliminary
step in analyzing the gene interactions, the method applies two different techniques, a clustering algorithm and an auto regressive
(AR) model. This approach provides a successful prediction of the dynamic pathways involved in the biological process under
study. At this level, these pure computational techniques lack the transparency required for analysis and understanding of
the gene interactions. To overcome the limitations, we have designed a visual analysis method that applies several visualization
techniques, including pixel-based gene representation, animation, and multi-dimensional scaling (MDS), in a new way. This
visual analysis framework allows the user to quickly and thoroughly search for and find the dynamic interactions among genes,
highlight interesting gene information, show the detailed annotations of the selected genes, compare regulatory behaviors
for different genes, and support gene sequence analysis for the interesting genes. In order to enhance these analysis capabilities,
several methods are enabled, providing a simple graph display, a pixel-based gene visualization technique, and a relation-displaying
technique among gene expressions and gene regulatory pathways. 相似文献
90.