title: Protein homology detection with biologically inspired features and interpretable statistical models.
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.
authors.affiliation: Department of Computer Science, Rutgers University, Piscataway, NJ 08854-8019, USA. paihuang@cs.rutgers.edu
authors.forename: Pai-Hsi
authors.initials: PH
authors.lastname: Huang
authors.forename: Vladimir
authors.initials: V
authors.lastname: Pavlovic