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We propose algorithms for learning Markov boundaries from data without having to learn a Bayesian network first. We study their correctness, scalability and data efficiency. The last two properties are important because we aim to apply the algorithms to identify the minimal set of features that is needed for probabilistic classification in databases with(More)
We analyze two different feature selection problems: finding a minimal feature set optimal for classification (MINIMAL-OPTIMAL) vs. finding all features relevant to the target variable (ALL-RELEVANT). The latter problem is motivated by recent applications within bioinformatics, particularly gene expression analysis. For both problems, we identify classes of(More)
MOTIVATION For the last few years, Bayesian networks (BNs) have received increasing attention from the computational biology community as models of gene networks, though learning them from gene-expression data is problematic. Most gene-expression databases contain measurements for thousands of genes, but the existing algorithms for learning BNs from data do(More)
We propose an algorithm for learning the Markov boundary of a random variable from data without having to learn a complete Baye-sian network. The algorithm is correct under the faithfulness assumption, scalable and data efficient. The last two properties are important because we aim to apply the algorithm to identify the minimal set of random variables that(More)
We present a sound and complete graphical criterion for reading dependencies from the minimal undirected independence map G of a graphoid M that satisfies weak transitivity. Here, complete means that it is able to read all the dependencies in M that can be derived by applying the graphoid properties and weak transitivity to the dependencies used in the(More)
Recently, important insights into static network topology for biological systems have been obtained, but still global dynamical network properties determining stability and system responsiveness have not been accessible for analysis. Herein, we explore a genome-wide gene-to-gene regulatory network based on expression data from the cell cycle in(More)
A deficiency in microsomal triglyceride transfer protein (MTP) causes the human lipoprotein deficiency syndrome abetalipoproteinemia. However, the role of MTP in the assembly and secretion of VLDL in the liver is not precisely understood. It is not clear, for instance, whether MTP is required to move the bulk of triglycerides into the lumen of the(More)
BACKGROUND Alimentary lipemia has been associated with coronary heart disease and common carotid artery intima-media thickness (IMT). This study was designed to investigate the relations of subclasses of postprandial triglyceride-rich lipoproteins (TRLs) with IMT. METHODS AND RESULTS Ninety-six healthy 50-year-old men with an apolipoprotein (apo) E3/E3(More)
After the major achievements of the DNA sequencing projects, an equally important challenge now is to uncover the functional relationships among genes (i.e. gene networks). It has become increasingly clear that computational algorithms are crucial for extracting meaningful information from the massive amount of data generated by high-throughput genome-wide(More)
Despite the well-documented effects of plasma lipid lowering regimes halting atherosclerosis lesion development and reducing morbidity and mortality of coronary artery disease and stroke, the transcriptional response in the atherosclerotic lesion mediating these beneficial effects has not yet been carefully investigated. We performed transcriptional(More)