Inferring gene regulation networks
نویسنده
چکیده
A current challenge in system biology is to infer the regulation network of a family of p genes from a n-sample of microarrays, with n (much) smaller than p. Gaussian graphical models are simple models to describe these regulation networks. We propose a procedure that performs Gaussian graph estimation by model selection. We introduce a collection of candidate graphs and then select one of them by minimizing a penalized empirical risk. We pay a special attention to the maximum degree of the graphs that we can handle and asses the performance of the procedure in a non-asymptotic setting. The good theoretical properties of the procedure are confirmed on numerical examples.
منابع مشابه
Defining a robust biological prior from Pathway Analysis to drive Network Inference
Abstract: Inferring genetic networks from gene expression data is one of the most challenging work in the post-genomic era, partly due to the vast space of possible networks and the relatively small amount of data available. In this field, Gaussian Graphical Model (GGM) provides a convenient framework for the discovery of biological networks. In this paper, we propose an original approach for i...
متن کاملInference of Cancer-specific Gene Regulatory Networks Using Soft Computing Rules
Perturbations of gene regulatory networks are essentially responsible for oncogenesis. Therefore, inferring the gene regulatory networks is a key step to overcoming cancer. In this work, we propose a method for inferring directed gene regulatory networks based on soft computing rules, which can identify important cause-effect regulatory relations of gene expression. First, we identify important...
متن کاملUsing a State-Space Model and Location Analysis to Infer Time-Delayed Regulatory Networks
Computational gene regulation models provide a means for scientists to draw biological inferences from time-course gene expression data. Based on the state-space approach, we developed a new modeling tool for inferring gene regulatory networks, called time-delayed Gene Regulatory Networks (tdGRNs). tdGRN takes time-delayed regulatory relationships into consideration when developing the model. I...
متن کاملInferring gene networks from time series microarray data using dynamic Bayesian networks
Dynamic Bayesian networks (DBNs) are considered as a promising model for inferring gene networks from time series microarray data. DBNs have overtaken Bayesian networks (BNs) as DBNs can construct cyclic regulations using time delay information. In this paper, a general framework for DBN modelling is outlined. Both discrete and continuous DBN models are constructed systematically and criteria f...
متن کاملInferring qualitative relations in genetic networks and metabolic pathways
MOTIVATION Inferring genetic network architecture from time series data of gene expression patterns is an important topic in bioinformatics. Although inference algorithms based on the Boolean network were proposed, the Boolean network was not sufficient as a model of a genetic network. RESULTS First, a Boolean network model with noise is proposed, together with an inference algorithm for it. ...
متن کاملBGRMI: A method for inferring gene regulatory networks from time-course gene expression data and its application in breast cancer research
Reconstructing gene regulatory networks (GRNs) from gene expression data is a challenging problem. Existing GRN reconstruction algorithms can be broadly divided into model-free and model-based methods. Typically, model-free methods have high accuracy but are computation intensive whereas model-based methods are fast but less accurate. We propose Bayesian Gene Regulation Model Inference (BGRMI),...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2008