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Constructions of gene and protein dynamic network is a challenging and important problem in genomic research while estimating the temporal correlations and non-stationarity are the keys in this process. In this paper, we develop Bayesian dynamic multivariate models to tackle this challenge for inferring the gene network profiles associated with diseases and treatments. We treat both the stochastic...
Constructions of gene and protein dynamic network is a challenging and important problem in genomic research while estimating the temporal correlations and non-stationarity are the keys in this process. In this paper, we develop Bayesian dynamic multivariate models to tackle this challenge for inferring the gene network profiles associated with diseases and treatments. We treat both the stochastic...
Prediction of class II MHC-peptide binding is a challenging task due to variable length of binding peptides. Different computational methods have been developed; however, each has its own strength and weakness. In order to provide reliable prediction, it is important to design a system that enables the integration of outcomes from various predictors. In this paper, we introduce a procedure of building...
Prediction of class II MHC-peptide binding is a challenging task due to variable length of binding peptides. Different computational methods have been developed; however, each has its own strength and weakness. In order to provide reliable prediction, it is important to design a system that enables the integration of outcomes from various predictors. In this paper, we introduce a procedure of building...
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