Inferring Gene Regulatory Network from Bayesian Network Model Based on Re-sampling
نویسندگان
چکیده
منابع مشابه
Inferring Gene Regulatory Network from Bayesian Network Model Based on Re-Sampling
Nowadays, gene chip technology has rapidly produced a wealth of information about gene expression activities. But the time-series expression data present a phenomenon that the number of genes is in thousands and the number of experimental data is only a few dozen. For such cases, it is difficult to learn network structure from such data. And the result is not ideal. So it needs to take measures...
متن کاملInferring Gene Regulatory Networks from Gene Expression Data by a Dynamic Bayesian Network-Based Model
Enabled by recent advances in bioinformatics, the inference of gene regulatory networks (GRNs) from gene expression data has garnered much interest from researchers. This is due to the need of researchers to understand the dynamic behavior and uncover the vast information lay hidden within the networks. In this regard, dynamic Bayesian network (DBN) is extensively used to infer GRNs due to its ...
متن کاملInferring Gene Regulatory Network Structure
Inferring the network structure of gene regulatory networks is one of the most important problems in contemporary bioinformatics. We analyze different methodologies for inferring small to very large sized gene networks. We use the datasets of DREAM 3 in-silico network challenge that is provided online [1]. The challenge involves inferring primarily the network structure from steady state gene e...
متن کاملInference of Gene Regulatory Network Based on Local Bayesian Networks
The inference of gene regulatory networks (GRNs) from expression data can mine the direct regulations among genes and gain deep insights into biological processes at a network level. During past decades, numerous computational approaches have been introduced for inferring the GRNs. However, many of them still suffer from various problems, e.g., Bayesian network (BN) methods cannot handle large-...
متن کاملInferring Gene Regulatory Networks from Multiple Data Sources Via a Dynamic Bayesian Network with Structural EM
Using our dynamic Bayesian network with structural Expectation Maximization (SEM-DBN), we develop a new framework to model gene regulatory network from both gene expression data and transcriptional factor binding site data. Only based on mRNA expression data, it is not enough to accurately estimate a gene network. It is difficult for us to estimate a gene network accurately only with the mRNA e...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: TELKOMNIKA (Telecommunication, Computing, Electronics and Control)
سال: 2013
ISSN: 2302-9293,1693-6930
DOI: 10.12928/telkomnika.v11i1.769