Improved Prediction of Eukaryotic Protein Subcellular Localization Using Particle Swarm Optimization of Multiple Classifiers

Nuannimnoi, Sirapop; Lertampaiporn, Supatcha; Thammarongtham, Chinae

doi:10.1109/icsec.2017.8443775

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…Diverse subcellular localization computational prediction tools were proposed using different training data procedures, data features, and machine learning algorithms [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ]. Some tools used the support vector machine (SVM) algorithm such as MultiLoc2 [ 10 ], Plant-mSubP [ 11 ], mGOASVM [ 12 ], WegoLoc [ 13 ], and LocTree [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Ensemble of Multiple Classifiers for Multilabel Classification of Plant Protein Subcellular Localization

Wattanapornprom

Thammarongtham

Hongsthong

et al. 2021

Life

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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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Section: Introductionmentioning

confidence: 99%

Ensemble of Multiple Classifiers for Multilabel Classification of Plant Protein Subcellular Localization

Wattanapornprom

Thammarongtham

Hongsthong

et al. 2021

Life

Self Cite

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“…Moreover, PSO does not rely on the gradient of the problem to be optimized; thus, PSO does not require that the optimization problem be differentiable, as is required by classic optimization methods [17–19]. Recently, a PSO-based consensus method has been successfully applied to classify eukaryotic protein localization results [20].…”

Section: Introductionmentioning

confidence: 99%

PSO-LocBact: A Consensus Method for Optimizing Multiple Classifier Results for Predicting the Subcellular Localization of Bacterial Proteins

Lertampaiporn

Nuannimnoi

Vorapreeda

et al. 2019

BioMed Research International

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Several computational approaches for predicting subcellular localization have been developed and proposed. These approaches provide diverse performance because of their different combinations of protein features, training datasets, training strategies, and computational machine learning algorithms. In some cases, these tools may yield inconsistent and conflicting prediction results. It is important to consider such conflicting or contradictory predictions from multiple prediction programs during protein annotation, especially in the case of a multiclass classification problem such as subcellular localization. Hence, to address this issue, this work proposes the use of the particle swarm optimization (PSO) algorithm to combine the prediction outputs from multiple different subcellular localization predictors with the aim of integrating diverse prediction models to enhance the final predictions. Herein, we present PSO-LocBact, a consensus classifier based on PSO that can be used to combine the strengths of several preexisting protein localization predictors specially designed for bacteria. Our experimental results indicate that the proposed method can resolve inconsistency problems in subcellular localization prediction for both Gram-negative and Gram-positive bacterial proteins. The average accuracy achieved on each test dataset is over 98%, higher than that achieved with any individual predictor.

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Weighted Ensemble for Plant Protein Subcellular Localization Using Particle Swarm Optimization

Wattanapornprom

Glomrit

Prayongsup

et al. 2021

2021 18th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technolo

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Improved Prediction of Eukaryotic Protein Subcellular Localization Using Particle Swarm Optimization of Multiple Classifiers

Cited by 3 publications

References 11 publications

Ensemble of Multiple Classifiers for Multilabel Classification of Plant Protein Subcellular Localization

Ensemble of Multiple Classifiers for Multilabel Classification of Plant Protein Subcellular Localization

PSO-LocBact: A Consensus Method for Optimizing Multiple Classifier Results for Predicting the Subcellular Localization of Bacterial Proteins

Weighted Ensemble for Plant Protein Subcellular Localization Using Particle Swarm Optimization

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