Biomedical subjects

Naijun Sha

Publications and source records attributed to Naijun Sha.

4 recordsLinked to original sources

PubMed2006

Bayesian variable selection for the analysis of microarray data with censored outcomes.

MOTIVATION: A common task in microarray data analysis consists of identifying genes associated with a phenotype. When the outcomes of interest are censored time-to-event data, standard approaches assess the effect of genes by fitting univariate survival models. In this paper, we propose a Bayesian variable selection approach, which allows the identification of relevant markers by jointly assessing sets of genes. We consider accelerated failure time (AFT) models with log-normal and log-t distributional assumptions. A data augmentation approach is used to impute the failure times of censored observations and mixture priors are used for the regression coefficients to identify promising subsets of variables. The proposed method provides a unified procedure for the selection of relevant genes and the prediction of survivor functions. RESULTS: We demonstrate the performance of the method on simulated examples and on several microarray datasets. For the simulation study, we consider scenarios with large number of noisy variables and different degrees of correlation between the relevant and non-relevant (noisy) variables. We are able to identify the correct covariates and obtain good prediction of the survivor functions. For the microarray applications, some of our selected genes are known to be related to the diseases under study and a few are in agreement with findings from other researchers. AVAILABILITY: The Matlab code for implementing the Bayesian variable selection method may be obtained from the corresponding author. CONTACT: mvannucci@stat.tamu.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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PubMed2004

Modeling antitumor activity by using a non-linear mixed-effects model.

The response of solid tumors to antitumor treatment generally declines markedly with treatment time. Sometimes, a tumor regrows (rebounds) before the end of the treatment period. Studies of the patterns of tumor response to treatment are important, because they may provide useful information for clinical decision-making. We have investigated patterns of tumor response in mouse xenograft tumors by using data from a study conducted at St. Jude Children's Research Hospital. We applied a biexponential non-linear mixed-effects model to an analysis of changes in tumor volume over a given period of treatment. The model gives a good fit to the data, even for small sample sizes. We addressed the relation between the baseline tumor volumes and the decay rates of the first and second stages of the tumor's response to treatment, and we applied sensitive analysis to determine the effect of using different imputed values for missing data. We also proposed a novel approach to a comparison of the antitumor effects of three different treatments, and we used the data from a St. Jude study to demonstrate the potential of this comparison approach in cancer clinical decision-making.

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PubMed2004

Bayesian variable selection in multinomial probit models to identify molecular signatures of disease stage.

Here we focus on discrimination problems where the number of predictors substantially exceeds the sample size and we propose a Bayesian variable selection approach to multinomial probit models. Our method makes use of mixture priors and Markov chain Monte Carlo techniques to select sets of variables that differ among the classes. We apply our methodology to a problem in functional genomics using gene expression profiling data. The aim of the analysis is to identify molecular signatures that characterize two different stages of rheumatoid arthritis.

Arthritis, Rheumatoid↗

PubMed2003

Gene selection: a Bayesian variable selection approach.

UNLABELLED: Selection of significant genes via expression patterns is an important problem in microarray experiments. Owing to small sample size and the large number of variables (genes), the selection process can be unstable. This paper proposes a hierarchical Bayesian model for gene (variable) selection. We employ latent variables to specialize the model to a regression setting and uses a Bayesian mixture prior to perform the variable selection. We control the size of the model by assigning a prior distribution over the dimension (number of significant genes) of the model. The posterior distributions of the parameters are not in explicit form and we need to use a combination of truncated sampling and Markov Chain Monte Carlo (MCMC) based computation techniques to simulate the parameters from the posteriors. The Bayesian model is flexible enough to identify significant genes as well as to perform future predictions. The method is applied to cancer classification via cDNA microarrays where the genes BRCA1 and BRCA2 are associated with a hereditary disposition to breast cancer, and the method is used to identify a set of significant genes. The method is also applied successfully to the leukemia data. SUPPLEMENTARY INFORMATION: http://stat.tamu.edu/people/faculty/bmallick.html.

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