Results 1  10
of
18
Improving predictive inference under covariate shift by weighting the loglikelihood function
 JOURNAL OF STATISTICAL PLANNING AND INFERENCE
, 2000
"... ..."
Combining microarrays and biological knowledge for estimating gene networks via Bayesian networks
 In Proceedings of the IEEE Computer Society Bioinformatics Conference (CSB 03
, 2003
"... We propose a statistical method for estimating a gene network based on Bayesian networks from microarray gene expression data together with biological knowledge including proteinprotein interactions, proteinDNA interactions, binding site information, existing literature and so on. Unfortunately, m ..."
Abstract

Cited by 60 (5 self)
 Add to MetaCart
(Show Context)
We propose a statistical method for estimating a gene network based on Bayesian networks from microarray gene expression data together with biological knowledge including proteinprotein interactions, proteinDNA interactions, binding site information, existing literature and so on. Unfortunately, microarray data do not contain enough information for constructing gene networks accurately in many cases. Our method adds biological knowledge to the estimation method of gene networks under a Bayesian statistical framework, and also controls the tradeoff between microarray information and biological knowledge automatically. We conduct Monte Carlo simulations to show the effectiveness of the proposed method. We analyze Saccharomyces cerevisiae gene expression data as an application. 1.
Dynamic Bayesian Network and Nonparametric Regression for Nonlinear Modeling of Gene Networks from Time Series Gene Expression Data
 Biosystems
, 2003
"... Abstract. We propose a dynamic Bayesian network and nonparametric regression model for constructing a gene network from time series microarray gene expression data. The proposed method can overcome a shortcoming of the Bayesian network model in the sense of the construction of cyclic regulations. Th ..."
Abstract

Cited by 56 (9 self)
 Add to MetaCart
(Show Context)
Abstract. We propose a dynamic Bayesian network and nonparametric regression model for constructing a gene network from time series microarray gene expression data. The proposed method can overcome a shortcoming of the Bayesian network model in the sense of the construction of cyclic regulations. The proposed method can analyze the microarray data as continuous data and can capture even nonlinear relations among genes. It can be expected that this model will give a deeper insight into the complicated biological systems. We also derive a new criterion for evaluating an estimated network from Bayes approach. We demonstrate the effectiveness of our method by analyzing Saccharomyces cerevisiae gene expression data. 1
Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network
 Proc. 1st IEEE Computer Society Bioinformatics Conference
, 2002
"... We propose a new statistical method for constructing a genetic network from microarray gene expression data by using a Bayesian network. An essential point of Bayesian network construction is in the estimation of the conditional distribution of each random variable. We consider fitting nonparametric ..."
Abstract

Cited by 39 (18 self)
 Add to MetaCart
(Show Context)
We propose a new statistical method for constructing a genetic network from microarray gene expression data by using a Bayesian network. An essential point of Bayesian network construction is in the estimation of the conditional distribution of each random variable. We consider fitting nonparametric regression models with heterogeneous error variances to the microarray gene expression data to capture the nonlinear structures between genes. A problem still remains to be solved in selecting an optimal graph, which gives the best representation of the system among genes. We theoretically derive a new graph selection criterion from Bayes approach in general situations. The proposed method includes previous methods based on Bayesian networks. We demonstrate the effectiveness of the proposed method through the analysis of Saccharomyces cerevisiae gene expression data newly obtained by disrupting 100 genes. 1.
Bayesian and Frequentist Approaches to Parametric Predictive Inference
 BAYESIAN STATISTICS, J. M. BERNARDO , J. O. BERGER , A. P. DAWID , A. F. M. SMITH (EDS.)
, 1998
"... ..."
A Representation of the Posterior Mean for a Location Model
, 1991
"... liez's theorem. Directions for future development are indicated. Some key words: Bayesian inference; Conditional inference; Robustness; Score function. 1. INTRODUCTION An exact representation for the posterior mean, E(Oly), is given where y is a 1 x n vector of observations from a location m ..."
Abstract

Cited by 8 (7 self)
 Add to MetaCart
liez's theorem. Directions for future development are indicated. Some key words: Bayesian inference; Conditional inference; Robustness; Score function. 1. INTRODUCTION An exact representation for the posterior mean, E(Oly), is given where y is a 1 x n vector of observations from a location model, f(x0), and 0 has a prior density, p(0),that is a normal scale mixture. Let L(O) denote the likelihood function and let y = (, a) where 0 is the maximum likelihood estimator and a is the maximal ancillary. The representation makes use of two results: the conditional distribution of the maximum likelihood estimator, p([O, a) (BarndorffNielsen, 1983), and a result of Masreliez (1975). It is shown that, under a normaLprior , E(O[y) ca.n be represented as a linear transformation of the score function of p(O]a), where p(O[a)= p([O, a)p(O) dO. The representation can be viewed as a generalization of Masreliez's result that deals with the model, X = 0 + e, 0 N(m, 2) and represents the posterior m
Bayesian Inference for SmallSample CaptureRecapture Data
"... this paper is based on the Laplace approximation for integrals. Consider a smooth convex function h(\Delta) of a pdimensional parameter u with a minimum at u, where ..."
Abstract

Cited by 2 (1 self)
 Add to MetaCart
this paper is based on the Laplace approximation for integrals. Consider a smooth convex function h(\Delta) of a pdimensional parameter u with a minimum at u, where
Editorial Board of
"... current interests include modelling gene networks from time series microarray gene expression data using statistical methods, including DBNs. Seiya Imoto is currently a research associate at the laboratory of ..."
Abstract
 Add to MetaCart
current interests include modelling gene networks from time series microarray gene expression data using statistical methods, including DBNs. Seiya Imoto is currently a research associate at the laboratory of
Estimating Gene Networks from Gene . . .
 BIOINFORMATICS
, 2003
"... We present a statistical method for estimating gene networks and detecting promoter elements simultaneously. When estimating a network from gene expression data alone, a common problem is that the number of microarrays is limited compared to the number of variables in the network model, making accur ..."
Abstract
 Add to MetaCart
We present a statistical method for estimating gene networks and detecting promoter elements simultaneously. When estimating a network from gene expression data alone, a common problem is that the number of microarrays is limited compared to the number of variables in the network model, making accurate estimation a difficult task. Our method overcomes this problem by integrating the microarray gene expression data and the DNA sequence information into a Bayesian network model. The basic idea of our method is that, if a parent gene is a transcription factor, its children may share a consensus motif in their promoter regions of the DNA sequences. Our method detects consensus motifs based on the structure of the estimated network, then reestimates the network using the result of the motif detection. We continue this iteration until the network becomes stable. To show the effectiveness of our method, we conducted Monte Carlo simulations and applied our method to Saccharomyces cerevisiae data as areal application.