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This paper describes a Bayesian method for combining an arbitrary mixture of observational and experimental data in order to learn causal Bayesian networks. Observational data are passively observed. Experimental data, such as that produced by randomized controlled trials, result from the experimenter manipulating one or more variables (typically randomly)(More)
This paper reports the methods and results of a computer-based search for causal relationships in the gene-regulation pathway of galactose metabolism in the yeast Saccharomyces cerevisiae. The search uses recently published data from cDNA microarray experiments. A Bayesian method was applied to learn causal networks from a mixture of observational and(More)
In this study we have identified key genes that are critical in development of astrocytic tumors. Meta-analysis of microarray studies which compared normal tissue to astrocytoma revealed a set of 646 differentially expressed genes in the majority of astrocytoma. Reverse engineering of these 646 genes using Bayesian network analysis produced a gene network(More)
This paper reports the methods and results of a computer-based algorithm that takes as input the expression levels of a set of genes as given by DNA microarray data, and then searches for causal pathways that represent how the genes regulate each other. The algorithm uses local heuristic search and a Bayesian scoring metric. We applied the algorithm to(More)
The main topic of this paper is modeling the expected value of experimentation (EVE) for discovering causal pathways in gene expression data. By experimentation we mean both interventions (e.g., a gene knockout experiment) and observations (e.g., passively observing the expression level of a "wild-type" gene). We introduce a system called GEEVE (causal(More)
The main topic of this paper is evaluating a system that uses the expected value of experimentation for discovering causal pathways in gene expression data. By experimentation we mean both interventions (e.g., a gene knock-out experiment) and observations (e.g., passively observing the expression level of a "wild-type" gene). We introduce a system called(More)
HIV infection and drugs of abuse such as methamphetamine (METH), cocaine, and alcohol use have been identified as risk factors for triggering inflammation. Acute phase proteins such as C-reactive protein (CRP) and serum amyloid A (SAA) are the biomarkers of inflammation. Hence, the interactive effect of drugs of abuse with acute phase proteins in(More)
In this article we introduce modern statistical machine learning and bioinformatics approaches that have been used in learning statistical relationships from big data in medicine and behavioral science that typically include clinical, genomic (and proteomic) and environmental variables. Every year, data collected from biomedical and behavioral science is(More)
Arachidonic acid (AA) is known to be increased in HIV infected patients and illicit drug users are linked with severity of viral replication, disease progression, and impaired immune functions. Studies have shown that cocaine accelerates HIV infection and disease progression mediated by immune cells. Dendritic cells (DC) are the first line of antigen(More)
This paper reports the methods and evaluation of a computer-based system that recommends microarray experimental design for biologists - causal discovery in Gene Expression data using Expected Value of Experimentation (GEEVE). The GEEVE system uses causal Bayesian networks and generates a decision tree for recommendations. To evaluate the GEEVE system, we(More)