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Ensemble of Multiple Classifiers for Multilabel Classification of Plant Protein Subcellular Localization
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Document Title
Ensemble of Multiple Classifiers for Multilabel Classification of Plant Protein Subcellular Localization
Author
Wattanapornprom W, Thammarongtham C, Hongsthong A, Lertampaiporn S
Name from Authors Collection
Affiliations
King Mongkuts University of Technology Thonburi; King Mongkuts University of Technology Thonburi; National Science & Technology Development Agency - Thailand; National Center Genetic Engineering & Biotechnology (BIOTEC)
Type
Article
Source Title
LIFE-BASEL
Year
2021
Volume
11
Issue
1
Open Access
Green Published, gold
Publisher
MDPI
DOI
10.3390/life11040293
Format
Abstract
The accurate prediction of protein localization is a critical step in any functional genome annotation process. This paper proposes an improved strategy for protein subcellular localization prediction in plants based on multiple classifiers, to improve prediction results in terms of both accuracy and reliability. The prediction of plant protein subcellular localization is challenging because the underlying problem is not only a multiclass, but also a multilabel problem. Generally, plant proteins can be found in 10-14 locations/compartments. The number of proteins in some compartments (nucleus, cytoplasm, and mitochondria) is generally much greater than that in other compartments (vacuole, peroxisome, Golgi, and cell wall). Therefore, the problem of imbalanced data usually arises. Therefore, we propose an ensemble machine learning method based on average voting among heterogeneous classifiers. We first extracted various types of features suitable for each type of protein localization to form a total of 479 feature spaces. Then, feature selection methods were used to reduce the dimensions of the features into smaller informative feature subsets. This reduced feature subset was then used to train/build three different individual models. In the process of combining the three distinct classifier models, we used an average voting approach to combine the results of these three different classifiers that we constructed to return the final probability prediction. The method could predict subcellular localizations in both single- and multilabel locations, based on the voting probability. Experimental results indicated that the proposed ensemble method could achieve correct classification with an overall accuracy of 84.58% for 11 compartments, on the basis of the testing dataset.
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Funding Sponsor
National Center for Genetic Engineering and Biotechnology (BIOTEC), CPM; National Science and Technology Development Agency (NSTDA), Bangkok, Thailand [P18-51620]; King Mongkut's University of Technology Thonburi, Thailand
License
CC BY
Rights
Authors
Publication Source
WOS