MultiP-SChlo: Multi-label protein subchloroplast localization prediction

Abstract

Chloroplasts are organelles in most green plant and some algal cells. Identifying protein subchloroplast localization in chloroplast organelle is very helpful for understanding the function of chloroplast proteins. There have existed a few computational prediction methods for protein subchloroplast localization. However, these existing works have ignored proteins with multiple subchloroplast locations when constructing prediction models, so that they can only predict one of all subchloroplast locations of this kind of multilabel proteins. To address this problem, through utilizing label-specific features and label correlations simultaneously, a novel multi-label classifier was developed for predicting protein subchloroplast location(s) with both single and multiple location sites. As an initial study, the overall accuracy of our proposed algorithm reach 55.52%, which is quite high to be able to become a promising tool for further studies.