Johan Björkegren

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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)
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)
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 (ALLRELEVANT). The latter problem is motivated by recent applications within bioinformatics, particularly gene expression analysis. For both problems, we identify classes of(More)
OBJECTIVES Remodeling of extracellular matrix (ECM) plays an important role in inflammatory disorders such as atherosclerosis. ADAMTS (a disintegrin and metalloproteinase with thrombospondin motifs) is a recently described family of proteinases that is able to degrade the ECM proteins aggrecan and versican expressed in blood vessels. The purpose of the(More)
Using deep sequencing (deepCAGE), the FANTOM4 study measured the genome-wide dynamics of transcription-start-site usage in the human monocytic cell line THP-1 throughout a time course of growth arrest and differentiation. Modeling the expression dynamics in terms of predicted cis-regulatory sites, we identified the key transcription regulators, their(More)
We propose an algorithm for learning the Markov boundary of a random variable from data without having to learn a complete Bayesian 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)
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)
Complete repertoires of molecular activity in and between tissues provided by new high-dimensional "omics" technologies hold great promise for characterizing human physiology at all levels of biological hierarchies. The combined effects of genetic and environmental perturbations at any level of these hierarchies can lead to vicious cycles of pathology and(More)
BACKGROUND Exaggerated postprandial triglyceridemia is common in normolipidemic patients with coronary artery disease (CAD). Alterations in the composition of triglyceride-rich lipoproteins (TRLs) are likely to underlie this metabolic disturbance. However, the composition of very-low-density lipoproteins (VLDLs), which are the most abundant postprandial(More)
Transcriptional regulatory networks govern cell differentiation and the cellular response to external stimuli. However, mammalian model systems have not yet been accessible for network analysis. Here, we present a genome-wide network analysis of the transcriptional regulation underlying the mouse macrophage response to bacterial lipopolysaccharide (LPS).(More)