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Title: Transductive learning for multi-label protein subchloroplast localization prediction
Authors: Wan, SB 
Mak, MW 
Kung, SY
Keywords: Protein subchloroplast localization prediction
Ensemble transductive learning
Profile alignment
Multi-label classification
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE/ACM transactions on computational biology and bioinformatics, 2017, v. 14, no. 1, p. 212-224 How to cite?
Journal: IEEE/ACM transactions on computational biology and bioinformatics 
Abstract: Predicting the localization of chloroplast proteins at the sub-subcellular level is an essential yet challenging step to elucidate their functions. Most of the existing subchloroplast localization predictors are limited to predicting single-location proteins and ignore the multi-location chloroplast proteins. While recent studies have led to some multi-location chloroplast predictors, they usually perform poorly. This paper proposes an ensemble transductive learning method to tackle this multi-label classification problem. Specifically, given a protein in a dataset, its composition-based sequence information and profile-based evolutionary information are respectively extracted. These two kinds of features are respectively compared with those of other proteins in the dataset. The comparisons lead to two similarity vectors which are weighted-combined to constitute an ensemble feature vector. A transductive learning model based on the least squares and nearest neighbor algorithms is proposed to process the ensemble features. We refer to the resulting predictor as as EnTrans-Chlo. Experimental results on a stringent benchmark dataset and a novel dataset demonstrate that EnTrans-Chlo significantly outperforms state-of-the-art predictors and particularly gains more than 4 percent (absolute) improvement on the overall actual accuracy. For readers' convenience, EnTrans-Chlo is freely available online at
ISSN: 1545-5963
EISSN: 1557-9964
DOI: 10.1109/TCBB.2016.2527657
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