H. Lahdesmaki

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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(More)
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(More)
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(More)
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