research-article

Transductive Learning for Multi-Label Protein Subchloroplast Localization Prediction

Authors:

Sun-Yuan KungAuthors Info & Claims

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), Volume 14, Issue 1

Pages 212 - 224

https://doi.org/10.1109/TCBB.2016.2527657

Published: 01 January 2017 Publication History

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 http://bioinfo.eie.polyu.edu.hk/EnTransChloServer/.

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Cited By

Han LGuo Z(2022)GAN-PCL: An Efficient Protein Subchloroplast Site Predictor with GAN-based Data Augmented and Feature FusionProceedings of the 14th International Conference on Bioinformatics and Biomedical Technology10.1145/3543377.3543388(72-78)Online publication date: 27-May-2022
https://dl.acm.org/doi/10.1145/3543377.3543388
Kumar RGarg AKumari BJain AKumar MContribution E(2021)Identification of chloroplast and sub-chloroplast proteins from sequence-attributed features using support vector machine and domain information2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)10.1109/CIBCB49929.2021.9562787(1-9)Online publication date: 13-Oct-2021
https://dl.acm.org/doi/10.1109/CIBCB49929.2021.9562787
Sun JDu P(2020)Predicting protein subchloroplast locations: the 10th anniversaryFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-020-9507-015:2Online publication date: 2-Oct-2020
https://dl.acm.org/doi/10.1007/s11704-020-9507-0
Show More Cited By

Transductive Learning for Multi-Label Protein Subchloroplast Localization Prediction
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning

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cover image IEEE/ACM Transactions on Computational Biology and Bioinformatics

IEEE/ACM Transactions on Computational Biology and Bioinformatics Volume 14, Issue 1

January 2017

229 pages

ISSN:1545-5963

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IEEE Computer Society Press

Washington, DC, United States

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Published: 01 January 2017

Published in TCBB Volume 14, Issue 1

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Cited By

Han LGuo Z(2022)GAN-PCL: An Efficient Protein Subchloroplast Site Predictor with GAN-based Data Augmented and Feature FusionProceedings of the 14th International Conference on Bioinformatics and Biomedical Technology10.1145/3543377.3543388(72-78)Online publication date: 27-May-2022
https://dl.acm.org/doi/10.1145/3543377.3543388
Kumar RGarg AKumari BJain AKumar MContribution E(2021)Identification of chloroplast and sub-chloroplast proteins from sequence-attributed features using support vector machine and domain information2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)10.1109/CIBCB49929.2021.9562787(1-9)Online publication date: 13-Oct-2021
https://dl.acm.org/doi/10.1109/CIBCB49929.2021.9562787
Sun JDu P(2020)Predicting protein subchloroplast locations: the 10th anniversaryFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-020-9507-015:2Online publication date: 2-Oct-2020
https://dl.acm.org/doi/10.1007/s11704-020-9507-0
Zhu LEster MHaspel NCowen LShehu AKahveci TPozzi G(2017)Bayesian Collective Markov Random Fields for Subcellular Localization Prediction of Human ProteinsProceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics10.1145/3107411.3107412(321-329)Online publication date: 20-Aug-2017
https://dl.acm.org/doi/10.1145/3107411.3107412

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