P. Baldi and A. D. Long. A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics, 17(6):509--519, 2001. Home/Search Document Details and Download
Summary Related Articles Check |
This paper is cited in the following contexts:
Gene Expression Data Analysis of Human Lymphoma Using Support.. - Valentini (2001) (Correct)
.... assess the reproduceability and the reliability of the data are far to be established, considering also that the repetitions number of the experiments are in general limited only to three or at most ve, because experiments remain costly or tedious to repeat, even with state of the art technology [4]. As a consequence, it is dicult to compare gene expression data produced by di erent laboratories or to use training data produced by a laboratory to classify gene expression data produced by another one. From a machine learning standpoint, it is dicult to evaluate the generalization performance ....
P. Baldi and A.D. Long. A bayesian Framework for the Analysis of Microarray Expression Data: Regularized t-Test and Statistical Inferences of Gene Changes. Bioinformatics, 17(6):509-519, 2001.
Cluster Analysis for Gene Expression Data: A Survey - Jiang, Zhang (2002) (4 citations) (Correct)
....features. Various methods have been taken to select features for microarray sample clustering. Statistical methods are used for identifying differentially expressed genes in both within slide and multiple slide experiment results to filter out genes that do not change much during the experiment [Baggerly2001, Baldi2001, Butte2001, Chen1997, Claverie1999, Dudoit2000, Efron2000, Herwig2001, Ideker2000, Manducchi2000, Newton2001, Park2001, Theilhaber2001, Thomas 31 2001, Tsien2001, Wittes1999, Xiong2001, Yue2001]. Another approach first groups the features into coherent sets using a clustering algorithm and then projects the samples onto a lower dimensional space spanned by the average expression patterns of the coherent feature sets [Hastie2000] Raychaudhuri et al. Raychaudhuri2000] applied principle ....
P. Baldi and A.D. Long. "A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes", Bioinformatics 2001 17: 509-519.
Microarrays: How Many Do You Need? - Zien, Fluck, Zimmer, Lengauer (2002) (Correct)
....measurement of the expression of many genes that is typical for microarrays in order to estimate distributions. Again exploiting the mass of data, but additionally assuming normal distributions, the t test can be regularized to account for the uncertainty associated with measured values [3]. In [23] a few statistical methods related to the t test are mathematically compared and applied to expression data. In [13] the significance of the likelihood ratio of null hypothesis versus differential expression is quantified, based on a general error model fitted to all genes. ....
P. Baldi and A. D. Long. A Bayesian Framework for the Analysis of Microarray Expression Data: Regularized t-Test and Statistical Inferences of Gene Changes. Bioinformatics, 2001.
Microarrays: How Many Do You Need? - Zien, Fluck, Zimmer, Lengauer (2002) (Correct)
....presented that make use of the simultaneous measurement of the expression of many genes that is typical for microarrays. Again exploiting the mass of data, but additionally assuming normal distributions, the t test can be regularized to account for the uncertainty associated with measured values [1]. In [9] the significance of the likelihood ratio of null hypothesis versus differential expression is quantified, based on a general error model fitted to all genes. In response to the noisiness of the data, statistical tests qualify the observed differences with estimates of the false positive ....
P. Baldi and A. D. Long. A Bayesian Framework for the Analysis of Microarray Expression Data: Regularized t-Test and Statistical Inferences of Gene Changes. Bioinformatics, 2001.
A Bayesian Approach to Transcript Estimation from Gene.. - Dror, Murnick, Rinaldi (2002) (3 citations) (Correct)
....model to yield a posterior probability distribution for the parameter of interest. A single estimate, an associated variance, and a confidence interval can be derived from this probability distribution. Other recent work has also taken a Bayesian approach to computing expression level changes [15, 2]. However, these authors use simple noise and prior models chosen for their computational convenience. In contrast, we derive noise and prior models directly from experimental data. BEAM is able to incorporate additional information about the experimental system without sacrificing computational ....
P. Baldi and A. D. Long. A Bayesian framework for the analysis of microarray expression data: Regularized t-test and statistical inferences of gene changes. Bioinformatics, 17:509--519, 2001.
The Use of Emerging Patterns in the Analysis of Gene.. - Dong, Li, Wong (2003) (Correct)
No context found.
P. Baldi and A. D. Long. A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics, 17(6):509--519, 2001.
A Theoretical Analysis of Gene Selection - Mukherjee, Roberts (Correct)
No context found.
P. Baldi and A. D. Long. A Bayesian Framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics, 17(6):509--19, 2001.
Bioinformatics: Organisms from Venus, Technology.. - De Moor, Marchal.. (2003) (Correct)
No context found.
Baldi,P. and Long,A.D. A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics 17, 509-519, 2001
Latent Herpes Simplex Virus Infection of Sensory Neurons.. - Kramer, Cook, al. (2003) (Correct)
No context found.
Baldi, P., and A. D. Long. 2001. A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics 17:509--519.
Analysis of Microarray Gene Expression Data - Huber, von Heydebreck, Vingron (2003) (Correct)
No context found.
Pierre Baldi and Anthony D. Long. A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics, 17:509--519, June 2001.
Estimating the Statistical Significance of Gene.. - Strand, Olson.. (2002) (Correct)
No context found.
Baldi, P. and Long, A.D. (2001) A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics, 17, 509--519.
cant di erential expression (as determined by the GG model).. - Log Odds Log (Correct)
No context found.
Baldi, P, Long AD. A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics 2001; 17(6): 509-519.
Evaluating Test Statistics to Select Interesting.. - Kooperberg.. (2002) (Correct)
No context found.
Baldi, P. and Long, A.D. (2001) A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics, 17, 509--519.
Unknown - It Is Well (Correct)
No context found.
Baldi, P. and A.D. Long. 2001. A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics 17:509-519.
Practical Approaches to Analyzing Results of Microarray.. - Kaminski, Friedman (Correct)
No context found.
Baldi, P., and A. D. Long. 2001. A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics 17:509--519.
Bayesian Estimation of Transcript Levels Using a.. - Dror, Murnick.. (2003) (Correct)
No context found.
P. Baldi and A. D. Long. A Bayesian framework for the analysis of microarray expression data: Regularized t-test and statistical inferences of gene changes. Bioinformatics, 17:509--519, 2001.
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC