Biomedical subjects

Jiang Gui

Publications and source records attributed to Jiang Gui.

3 recordsLinked to original sources

PubMed2005

Gradient directed regularization for sparse Gaussian concentration graphs, with applications to inference of genetic networks.

Large-scale microarray gene expression data provide the possibility of constructing genetic networks or biological pathways. Gaussian graphical models have been suggested to provide an effective method for constructing such genetic networks. However, most of the available methods for constructing Gaussian graphs do not account for the sparsity of the networks and are computationally more demanding or infeasible, especially in the settings of high dimension and low sample size. We introduce a threshold gradient descent (TGD) regularization procedure for estimating the sparse precision matrix in the setting of Gaussian graphical models and demonstrate its application to identifying genetic networks. Such a procedure is computationally feasible and can easily incorporate prior biological knowledge about the network structure. Simulation results indicate that the proposed method yields a better estimate of the precision matrix than the procedures that fail to account for the sparsity of the graphs. We also present the results on inference of a gene network for isoprenoid biosynthesis in Arabidopsis thaliana. These results demonstrate that the proposed procedure can indeed identify biologically meaningful genetic networks based on microarray gene expression data.

Arabidopsis↗

PubMed2005

Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data.

MOTIVATION: An important application of microarray technology is to relate gene expression profiles to various clinical phenotypes of patients. Success has been demonstrated in molecular classification of cancer in which the gene expression data serve as predictors and different types of cancer serve as a categorical outcome variable. However, there has been less research in linking gene expression profiles to the censored survival data such as patients' overall survival time or time to cancer relapse. It would be desirable to have models with good prediction accuracy and parsimony property. RESULTS: We propose to use the L(1) penalized estimation for the Cox model to select genes that are relevant to patients' survival and to build a predictive model for future prediction. The computational difficulty associated with the estimation in the high-dimensional and low-sample size settings can be efficiently solved by using the recently developed least-angle regression (LARS) method. Our simulation studies and application to real datasets on predicting survival after chemotherapy for patients with diffuse large B-cell lymphoma demonstrate that the proposed procedure, which we call the LARS-Cox procedure, can be used for identifying important genes that are related to time to death due to cancer and for building a parsimonious model for predicting the survival of future patients. The LARS-Cox regression gives better predictive performance than the L(2) penalized regression and a few other dimension-reduction based methods. CONCLUSIONS: We conclude that the proposed LARS-Cox procedure can be very useful in identifying genes relevant to survival phenotypes and in building a parsimonious predictive model that can be used for classifying future patients into clinically relevant high- and low-risk groups based on the gene expression profile and survival times of previous patients.

Biomarkers, Tumor↗

PubMed2004

Partial Cox regression analysis for high-dimensional microarray gene expression data.

MOTIVATION: An important application of microarray technology is to predict various clinical phenotypes based on the gene expression profile. Success has been demonstrated in molecular classification of cancer in which different types of cancer serve as categorical outcome variable. However, there has been less research in linking gene expression profile to censored survival outcome such as patients' overall survival time or time to cancer relapse. In this paper, we develop a partial Cox regression method for constructing mutually uncorrelated components based on microarray gene expression data for predicting the survival of future patients. RESULTS: The proposed partial Cox regression method involves constructing predictive components by repeated least square fitting of residuals and Cox regression fitting. The key difference from the standard principal components of Cox regression analysis is that in constructing the predictive components, our method utilizes the observed survival/censoring information. We also propose to apply the time-dependent receiver operating characteristic curve analysis to evaluate the results. We applied our methods to a publicly available dataset of diffuse large B-cell lymphoma. The outcomes indicated that combining the partial Cox regression method with principal components analysis results in parsimonious model with fewer components and better predictive performance. We conclude that the proposed partial Cox regression method can be very useful in building a parsimonious predictive model that can accurately predict the survival of future patients based on the gene expression profile and survival times of previous patients. AVAILABILITY: R codes are available upon request.

Biomarkers, Tumor↗