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Article
Open AccessA kernel-based integration of genome-wide data for clinical decision support
Although microarray technology allows the investigation of the transcriptomic make-up of a tumor in one experiment, the transcriptome does not completely reflect the underlying biology due to alternative splic...
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Chapter and Conference Paper
Semi-supervised Learning of Sparse Linear Models in Mass Spectral Imaging
We present an approach to learn predictive models and perform variable selection by incorporating structural information from Mass Spectral Imaging (MSI) data. We explore the use of a smooth quadratic penalty ...
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Article
Open AccessCandidate gene prioritization by network analysis of differential expression using machine learning approaches
Discovering novel disease genes is still challenging for diseases for which no prior knowledge - such as known disease genes or disease-related pathways - is available. Performing genetic studies frequently re...
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Article
Open AccessPredicting receptor-ligand pairs through kernel learning
Regulation of cellular events is, often, initiated via extracellular signaling. Extracellular signaling occurs when a circulating ligand interacts with one or more membrane-bound receptors. Identification of r...