S. Kim, E. R. Dougherty, M. L. Bittner, et al., "General nonlinear framework for the analysis of gene interaction via multivariate expression arrays," Journal of Biomedical Optics, vol. 5, no. 4, pp. 411--424, 2000. Home/Search Document Details and Download
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Reduction Mappings between Probabilistic Boolean Networks - Ivanov, Dougherty (2004) Self-citation (Dougherty) (Correct)
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S. Kim, E. R. Dougherty, M. L. Bittner, et al., "General nonlinear framework for the analysis of gene interaction via multivariate expression arrays," Journal of Biomedical Optics, vol. 5, no. 4, pp. 411--424, 2000.
Control Of Stationary Behavior In Probabilistic.. - Shmulevich.. (2002) Self-citation (Dougherty) (Correct)
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Kim S., Dougherty E. R., Bittner M. L., Chen Y., Sivakumar K., Meltzer P. and Trent J. M., General nonlinear framework for the analysis of gene interac- Control of Stationary Behavior in Probabilistic Boolean Networks 445 tion via multivariate expression arrays, Journal of Biomedical Optics 5(4) (2000) pp. 411--424.
From Boolean to Probabilistic Boolean Networks as.. - Shmulevich.. (2002) Self-citation (Dougherty) (Correct)
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S. Kim, E. R. Dougherty, M. L. Bittner, Y. Chen, K. Sivakumar, P. Meltzer, and J. M. Trent, "General nonlinear framework for the analysis of gene interaction via multivariate expression arrays," J. Biomed. Opt., vol. 5, no. 4, pp. 411--424, 2000.
Gene Prediction Using Multinomial Probit Regression with.. - Zhou, al. (2004) Self-citation (Dougherty) (Correct)
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S. Kim, E. R. Dougherty, M. L. Bittner, et al., "General nonlinear framework for the analysis of gene interaction via multivariate expression arrays," Biomedical Optics,vol.5,no.4, pp. 411--424, 2000.
Genomic Profiles and Predictive Biological.. - Johnson.. (2003) Self-citation (Dougherty) (Correct)
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Kim S, Dougherty ER, Bittner ML, Chen Y, Sivakumar K, Meltzer PS, and Trent JM. General nonlinear framework for the analysis of gene interaction via multivariate expression arrays. J Biomed Optics 5: 411--424, 2000.
Genomic Signal Processing: The Salient Issues - Dougherty, al. (2004) Self-citation (Dougherty Bittner) (Correct)
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S. Kim, E. R. Dougherty, M. L. Bittner, et al., "General nonlinear framework for the analysis of gene interaction via multivariate expression arrays," Biomedical Optics,vol.5,no.4, pp. 411--424, 2000.
From Boolean to Probabilistic Boolean Networks as.. - Shmulevich.. (2002) Self-citation (Dougherty) (Correct)
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S. Kim, E. R. Dougherty, M. L. Bittner, Y. Chen, K. Sivakumar, P. Meltzer, and J. M. Trent, "General nonlinear framework for the analysis of gene interaction via multivariate expression arrays," J. Biomed. Opt., vol. 5, no. 4, pp. 411--424, 2000.
Genomic Signal Processing: The Salient Issues - Dougherty, al. (2004) Self-citation (Dougherty Bittner) (Correct)
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S. Kim, E. R. Dougherty, M. L. Bittner, et al., "General nonlinear framework for the analysis of gene interaction via multivariate expression arrays," Biomedical Optics,vol.5,no.4, pp. 411--424, 2000.
Reduction Mappings between Probabilistic Boolean Networks - Ivanov, al. (2004) Self-citation (Dougherty) (Correct)
No context found.
S. Kim, E. R. Dougherty, M. L. Bittner, et al., "General nonlinear framework for the analysis of gene interaction via multivariate expression arrays," Journal of Biomedical Optics, vol. 5, no. 4, pp. 411--424, 2000.
Control Of Stationary Behavior In Probabilistic.. - Shmulevich.. (2002) Self-citation (Dougherty) (Correct)
No context found.
Kim S., Dougherty E. R., Bittner M. L., Chen Y., Sivakumar K., Meltzer P. and Trent J. M., General nonlinear framework for the analysis of gene interac- Control of Stationary Behavior in Probabilistic Boolean Networks 445 tion via multivariate expression arrays, Journal of Biomedical Optics 5(4) (2000) pp. 411--424.
Gene Prediction Using Multinomial Probit Regression with.. - Zhou, al. (2004) Self-citation (Dougherty) (Correct)
No context found.
S. Kim, E. R. Dougherty, M. L. Bittner, et al., "General nonlinear framework for the analysis of gene interaction via multivariate expression arrays," Biomedical Optics,vol.5,no.4, pp. 411--424, 2000.
Genomic Profiles and Predictive Biological.. - Johnson.. (2003) Self-citation (Dougherty) (Correct)
No context found.
Kim S, Dougherty ER, Bittner ML, Chen Y, Sivakumar K, Meltzer PS, and Trent JM. General nonlinear framework for the analysis of gene interaction via multivariate expression arrays. J Biomed Optics 5: 411--424, 2000.
Efficient Selection of Feature Sets Possessing.. - Hashimoto.. (2003) Self-citation (Dougherty Bittner Trent) (Correct)
....Y in the absenc of other observations and # X is the Bayes error for X. TheceE#T LA hashistoricM; been used to measure thee#ec of linear regression [16] and has beenrecE# # employed in nonlinear signalprocET;T; 5] and for measuring multivariateinteraciat among genes based on gene expression [5,8], and for crE struck; probabilistic Boolean networks [13] It is this last applic TEM that has motivated theceET#q analysis. It is evident from the de#nition that 0 6 # X 6 1 Z implies #X 6 #Z . In terms of theceET ceETk thefeature selec;Ec problem is to #nd a subset of k random variables ....
....among all subsets of size k. The particAEM applicAEM# we have in mind, and willdiscJq in detail following the development of the methodology, involves the predic EM whether a gene is up or down regulated based on the upor down regulation of other genes using data from gene expressionmic expres [8,9]. The fullsearc for this problem iscE#AT JE done using massively parallel hardware,pracre,E#; halts at m= 3 for about n = 600 genes, and takes 2 weeks using over 100 CPU s if all 600 targets are to becTJz#kEMJ [14] Sinc gene expression data is severely limited with cthETq genomic tecic#qEM ....
[Article contains additional citation context not shown here]
S. Kim, E.R. Dougherty, M.L. Bittner, Y. Chen, K. Sivakumar, P. Meltzer, J.M. Trent, General nonlinear framework for the analysis of gene interacTEM via multivariate expression arrays, J. Biomed. Opt. 5 (4)(OcJ-#A 2000) 411--424.
Can Markov Chain Models Mimic Biological Regulation? - Kim (2002) (1 citation) Self-citation (Kim Dougherty Bittner Chen) (Correct)
....chain simulation, genes from this pathway were chosen as a nucleus of the model system. Further genes for the model were chosen from a set of 587 genes from the melanoma data set that have been subjected to an analysis of their ability to cross predict each other s state in a multivariate setting [2, 7]. For the purposes of this analysis, each gene s expression level was quantized to a ternary value that represents the abundance of messenger RNA produced by that gene in a particular melanoma sample relative to the abundance of messenger RNA produced by that gene in a reference cell. The values ....
....out to choose a small set of genes for which both microarray data and some biological characterizations were available to guide finite state Markov chain modeling. General criteria to select important genes are: 1) their predictive relationships based on coe#cient of determination (CoD) analysis [2, 7], 2) their roles in classifying malignant melanoma [1] and (3) their biological functionalities. The first set of genes was chosen based on the analysis of multivariate measurement of gene expression relationships [8] which finds associations between the expression patterns of individual genes ....
Kim S., Dougherty E. R, Bittner M. L, Chen Y., Sivakumar K. L, Meltzer P. S. and Trent J. M., A general nonlinear framework for the analysis of gene interaction via expression array, J. Biomed Opt. 5 (2000) pp. 411--424.
Construction of Genomic Networks Using Mutual-Information.. - Zhou, Wang, Dougherty (2003) Self-citation (Dougherty) (Correct)
.... a posteriori (MAP) estimate of these parameters is obtained by maximizing the right hand side of (9) This can be done by using the reversible jump MCMC algorithm [2] 4 CoD for Predictors A natural way to select a set of predictors for a given gene is to employ the coe#cients of determination [7, 8]. Let x i be the target gene; X be sets of genes; and f be function rules are estimated by the reversible jump MCMC method developed in Section 3. Then probabilistic error measure # is defined as = E # # #g f x i # , 10) where g is a 1, 0, ....
....in the context of responsiveness to genotoxic stresses. The cell lines were chosen so that a sampling of both p53 proficient and p53 deficient cells would be assayed. By using the same data set used for the initial studies concerning nonlinear prediction in the context of genomic microarray data [7], we are able to compare the results of the proposed, informationMCMC approach with the previously obtained results. The ternary data of the survey (14 genes and 30 samples) are given in [7] where the conditions IR, MMS, and UV have the values 1 or 0, depending on whether the condition is or is ....
[Article contains additional citation context not shown here]
S. Kim, E.R. Dougherty, M.L. Bittner, Y. Chen, K. Sivakumar, P. Meltzer, and J.M. Trent, "General nonlinear framework for the analysis of gene interaction via multivariate expression arrays," Journal of Biomedical Optics, Vol. 5, No. 4, pp. 411-424, 2000.
Beyond Synexpression Relationships: Local.. - Qian.. (2001) (3 citations) (Correct)
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Kim, S., Dougherty, E. R., Bittner, M. L., Chen, Y., Sivakumar, K., Meltzer, P. & Trent, J. M. (2000). General nonlinear framework for the analysis of gene interaction via multivariate expression arrays. J. Biomed. Opt. 5, 411-424.
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