Protein homology detection with biologically inspired features and interpretable statistical models
by Pai-Hsi Huang, Vladimir Pavlovic
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 2, No. 2, 2008

Abstract: Computational classification of proteins using methods such as string kernels and Fisher-SVM has demonstrated great success. However, the resulting models do not offer an immediate interpretation of the underlying biological mechanisms. In this work, we propose a biologically motivated feature set combined with a sparse classifier, based on a small subset of positions and residues in protein sequences, for protein superfamily detection and show the performance of our models is comparable to that of the state-of-the-art methods on a benchmark dataset. The set of sparse critical features discovered by the models is consistent with the confirmed biological findings.

Online publication date: Sat, 28-Jun-2008

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