Titlebook: Computational Methods for Drug Repurposing; Quentin Vanhaelen Book 2019 Springer Science+Business Media, LLC, part of Springer Nature 2019

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https://doi.org/10.1007/978-0-387-34501-7st and long periods of new drug development. In silico drug repurposing further speeds up the process, by testing a large number of drugs against the biological signatures of known diseases. In this chapter, we present a step-by-step methodology of a transcriptomics-based computational drug repurpos

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https://doi.org/10.1007/978-0-387-34501-7and tumor suppressors are not classically druggable, in that they lack a targetable enzymatic activity and associated binding pockets that small molecule drugs can be directed to. This is especially relevant for transcription factors, which have long been thought to be undruggable. To address this g

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Analog Circuits in Weak Inversion,turn, create incentives for drug repositioning. Here, we use Gephi (a platform for complex network visualization and analysis) to represent a drug-drug interaction network with drug interaction information from DrugBank 4.1. Both modularity class- and force-directed layout ForceAtlas2 are employed t

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https://doi.org/10.1007/978-3-031-23898-7silico approaches for the identification of new drug-target interactions have been proposed, many of them based on a particular class of machine learning algorithms called kernel methods. These pattern classification algorithms are able to incorporate previous knowledge in the form of similarity fun

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https://doi.org/10.1007/978-3-031-23898-7er cost, faster, and more secured. We proposed a method for drug repositioning which can predict simple and complex relationships between drugs, drug targets, and diseases. Since biological networks typically present a suitable model for relationships between different biological concepts, our prima