The University of Tokyo · Medicine
이 교수의 연구실은 마이크로어레이 유전자 발현 데이터를 기반으로 한 유전자 네트워크 추정을 주요 연구 분야로 삼고 있으며, 베이지안 네트워크 기반의 비모수적 회귀 모델을 활용해 비선형 유전자 간 상호작용을 정밀하게 추론합니다. 생물학적 지식(단백질-단백질 상호작용, 전사 조절 정보 등)을 베이지안 프레임워크에 통합하여 데이터 부족 문제를 보완하고, 실험적 데이터와 생물학적 지식 간의 균형을 자동으로 조정하는 통계적 방법을 개발하고 있습니다. 특히, 유전자 선택 및 네트워크 구조 선택의 이론적 기반을 강화하여 생물학적 의미 있는 네트워크 추정에 기여하고 있습니다.
Figures are computed from collected data and may differ slightly.
We propose a new method for constructing genetic network from gene expression data by using Bayesian networks. We use nonparametric regression for capturing nonlinear relationships between genes and derive a new criterion for choosing the network in general situations. In a theoretical sense, our proposed theory and methodology include previous methods based on Bayes approach. We applied the proposed method to the S. cerevisiae cell cycle data and showed the effectiveness of our method by compar
Most of the conventional feature selection algorithms have a drawback whereby a weakly ranked gene that could perform well in terms of classification accuracy with an appropriate subset of genes will be left out of the selection. Considering this shortcoming, we propose a feature selection algorithm in gene expression data analysis of sample classifications. The proposed algorithm first divides genes into subsets, the sizes of which are relatively small (roughly of size h), then selects informat
We propose a statistical method for estimating a gene network based on Bayesian networks from microarray gene expression data together with biological knowledge including protein-protein interactions, protein-DNA interactions, binding site information, existing literature and so on. 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 f
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 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. Selecting the optimal graph, which gives the best represent
We propose a statistical method for estimating a gene network based on Bayesian networks from microarray gene expression data together with biological knowledge including protein-protein interactions, protein-DNA 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 Bayesia
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