Abstract

The identification of signal peptides and signal anchors is critical to understand the related biological mechanisms, and to find more effective vehicles for proteins production. Instead of studying signal peptides and signal anchors of single species separately, we developed a multi-tasking learning framework to investigate them across species simultaneously. By a multi-tasking feature selection method, we identified 12 important features out of 560 amino acid indices. The effectiveness and classification abilities of these 12 features were evaluated by another multi-tasking method, based on cross-validation and independent test. Further analysis of selected features brought some new insights into the physiochemical properties of signal peptides and signal anchors.

Keywords: Feature selection, multi tasking learning, signal anchor, signal peptide, SVM.