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Genetical genomics experiments have now been routinely conducted to measure both the genetic markers and gene expression data on the same subjects. The gene expression levels are often treated as quantitative traits and are subject to standard genetic analysis in order to identify the gene expression quantitative loci (eQTL). However, the genetic(More)
Motivated by analysis of gene expression data measured over different tissues or over time, we consider matrix-valued random variable and matrix-normal distribution, where the precision matrices have a graphical interpretation for genes and tissues, respectively. We present a l(1) penalized likelihood method and an efficient coordinate descent-based(More)
For a prediction problem of a given target feature in a large causal network under external interventions, we propose in this paper two partial orientation and local structural learning (POLSL) approaches, Local-Graph and PCD-by-PCD (where PCD denotes Parents , Children and some Descendants). The POLSL approaches are used to discover the local structure of(More)
Motivated by the analysis of genetical genomic data, we consider the problem of estimating high-dimensional sparse precision matrix adjusting for possibly a large number of covariates, where the covariates can affect the mean value of the random vector. We develop a two-stage estimation procedure to first identify the relevant covariates that affect the(More)
To detect goat vascular endothelial growth factor (VEGF)-mediated regrowth of hair, full-length VEGF164 cDNA was cloned from Inner Mongolia cashmere goat (Capra hircus) into the pET-his prokaryotic expression vector, and the recombinant plasmid was transferred into E. coli BL21 cells. The expression of recombinant 6×his-gVEGF164 protein was induced by 0.5(More)
For a given target node T and a given depth k ≥ 1, we propose an algorithm for discovering a local causal network around the target T to depth k. In our algorithm, we find parents, children and some descendants (PCD) of nodes stepwise away from the target T until all edges within the depth k local network cannot be oriented further. Our algorithm extends(More)
When we explore the causal relationship among time series variables, we first remove the potential seasonal term then we deal with the problem in the feature selection framework. For a time series with seasonal term, we use several sequences of sin(t) and cos(t) functions with different frequencies to design a 'pseudo' design matrix, and the seasonal term(More)
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