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Improved functional prediction of proteins by learning kernel combinations in multilabel settings
(2007)BMC BioinformaticsBackground We develop a probabilistic model for combining kernel matrices to predict the function of proteins. It extends previous approaches in that it can handle multiple labels which naturally appear in the context of protein function. Results Explicit modeling of multilabels significantly improves the capability of learning protein function from multiple kernels. The performance and the interpretability of the inference model are ...Conference Paper -
Time-series alignment by non-negative multiple generalized canonical correlation analysis
(2007)BMC BioinformaticsBackground Quantitative analysis of differential protein expressions requires to align temporal elution measurements from liquid chromatography coupled to mass spectrometry (LC/MS). We propose multiple Canonical Correlation Analysis (mCCA) as a method to align the non-linearly distorted time scales of repeated LC/MS experiments in a robust way. Results Multiple canonical correlation analysis is able to map several time series to a ...Conference Paper