Multisite protein subcellular localization prediction based on entropy density
Protein subcellular localization prediction is currently receiving much attention in the field of protein research. Many researchers make great efforts to study single-site protein subcellular localization, but the experimental data shows that many proteins can be found in two or more sub-cellular locations, prompting the study of multisite protein sub-cellular localization. This study utilized a Gpos-mPLOC data set and pseudo amino acid compositions, physicochemical properties of amino acid composition, and entropy density as three effective feature extraction methods. Then, these features were then placed in a multi-label k nearest neighbor classifier to predict subcellular protein locations. Experimental results verified that this approach provides a localization precision of 66.73% through the Jack-knife test.
