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Children with rhinovirus‐induced severe early wheezing have an increased risk of developing asthma later in life. The exact molecular mechanisms for this association are still mostly unknown. To identify potential changes in the transcriptional and epigenetic regulation in rhinovirus‐associated atopic or nonatopic asthma, we analyzed a cohort of 5‐year‐old children (n = 45) according to the virus...
In this paper, we study fundamental properties of the Self-Organizing Map (SOM) and the Generative Topographic Mapping (GTM), ramifications of the initialization of the algorithms and properties of the algorithms in the presence of missing data. We show that the commonly used principal component analysis (PCA) initialization of the GTM does not guarantee good learning results with high-dimensional...
An important preliminary goal in learning biological network models from experimental data is to study the plausibility of different types of regulatory mechanisms in living organisms. In addition to providing important biological insight, the knowledge of abundance of some specific regulatory rules in nature helps the computational problems by restricting the space of possible models to be learned...
Cluster analysis is widely applied to discover the function of previously unannotated genes. This paper presents a novel stratified beta-Gaussian mixture model, sBGMM, for clustering genes based on gene expression data, protein-DNA binding data and data that can provide information for constructing priors such as protein-protein interaction (PPI) data. An expectation maximization (EM) type of algorithm...
We formulate a probabilistic framework for transcription factor (TF) binding prediction that is built on the standard position specific frequency matrix (PSFM) and higher order Markovian background models. Contrary to the traditional hypothesis testing based methods which report a significance (p) value of TF binding at every possible base pair position in a promoter sequence, we develop a probabilistic...
Periodicity detection in time series measurements is a usual application of signal processing in studying biological data. The reasons for detecting periodically behaving biological events are many, e.g. periodicity in gene expression time series could suggest cell cycle control over the gene expression. In this paper we present a robust version of the Fisher's test for detecting hidden periodicities...
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