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BACKGROUND Identifying drug targets is a critical step in pharmacology. Drug phenotypic and chemical indexes are two important indicators in this field. However, in previous studies, the indexes were always isolated and the candidate proteins were often limited to a small subset of the human genome. METHODOLOGY/PRINCIPAL FINDINGS Based on the correlations(More)
MOTIVATION Understanding how drugs and diseases are associated in the molecular level is of critical importance to unveil disease mechanisms and treatments. Until recently, few studies attempt end to discover important gene modules shared by both drugs and diseases. RESULTS Here, we propose a novel presentation of drug-gene-disease relationship, a(More)
Vitexicarpin (VIT) isolated from the fruits of Vitex rotundifolia has shown antitumor, anti-inflammatory, and immunoregulatory properties. This work is designed to evaluate the antiangiogenic effects of VIT and address the underlying action mechanism of VIT by a network pharmacology approach. The results validated that VIT can act as a novel angiogenesis(More)
Identifying latent structure in large data matrices is essential for exploring biological processes. Here, we consider recovering gene co-expression networks from gene expression data, where each network encodes relationships between genes that are locally co-regulated by shared biological mechanisms. To do this, we develop a Bayesian statistical model for(More)
Identifying latent structure in high-dimensional genomic data is essential for exploring biological processes. Here, we consider recovering gene co-expression networks from gene expression data, where each network encodes relationships between genes that are co-regulated by shared biological mechanisms. To do this, we develop a Bayesian statistical model(More)
We develop a generalized method of moments (GMM) approach for fast parameter estimation in a new class of Dirichlet latent variable models with mixed data types. Parameter estimation via GMM has been demonstrated to have computational and statistical advantages over alternative methods, such as expectation maximization, variational inference, and Markov(More)
Latent factor models are the canonical statistical tool for exploratory analyses of lowdimensional linear structure for an observation matrix with p features across n samples. We develop a structured Bayesian group factor analysis model that extends the factor model to multiple coupled observation matrices; in the case of two observations, this reduces to a(More)
Autophagy is protective in cadmium (Cd)-induced oxidative damage. Endoplasmic reticulum (ER) stress has been shown to induce autophagy in a process requiring the unfolded protein response signalling pathways. Cd treatment significantly increased senescence in neuronal cells, which was aggravated by 3-MA or silencing of Atg5 and abolished by rapamycin. Cd(More)
Identifying latent structure in large data matrices is essential for exploring biological processes. Here, we consider recovering gene co-expression networks from gene expression data, where each network encodes relationships between genes that are locally co-regulated by shared biological mechanisms. To do this, we develop a Bayesian statistical model for(More)
BACKGROUND Postoperative pain can cause physiological distress, postoperative complications, and extended lengths of hospitalized stay. In children, management of postoperative pain is still recognized as being inadequate. OBJECTIVE The aim of this trial was to investigate the effects of intraperitoneal ropivacaine on postoperative pain, and recovery of(More)