An Ensemble Classifier for Eukaryotic Protein Subcellular Location Prediction Using Gene Ontology Categories and Amino Acid Hydrophobicity

Li, Liqi; Zhang, Yuan; Zou, Lingyun; Li, Changqing; Yu, Bo; Zheng, Xiaoqi; Zhou, Yue

doi:10.1371/journal.pone.0031057

Cited by 61 publications

(35 citation statements)

References 61 publications

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“…Being one type of general PseAAC [32], the GO (Gene Ontology) has been widely used to improve the prediction quality of protein subcellular localization (see, e.g., [23,25,26,[87][88][89][90][91]). The advantage of using the GO approach is that proteins mapped into the GO space (instead of Euclidean space or any other simple geometric space) would be better clustered according to their subcellular locations, as elaborated in [9,92].…”

Section: Proteins Sample Formulationmentioning

confidence: 99%

pLoc-mGpos: Incorporate Key Gene Ontology Information into General PseAAC for Predicting Subcellular Localization of Gram-Positive Bacterial Proteins

Xiao¹,

Cheng²,

Su³

et al. 2017

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The basic unit in life is cell. It contains many protein molecules located at its different organelles. The growth and reproduction of a cell as well as most of its other biological functions are performed via these proteins. But proteins in different organelles or subcellular locations have different functions. Facing the avalanche of protein sequences generated in the postgenomic age, we are challenged to develop high throughput tools for identifying the subcellular localization of proteins based on their sequence information alone. Although considerable efforts have been made in this regard, the problem is far apart from being solved yet. Most existing methods can be used to deal with single-location proteins only. Actually, proteins with multi-locations may have some special biological functions that are particularly important for drug targets. Using the ML-GKR (Multi-Label Gaussian Kernel Regression) method, we developed a new predictor called "pLoc-mGpos" by in-depth extracting the key information from GO (Gene Ontology) into the Chou's general PseAAC (Pseudo Amino Acid Composition) for predicting the subcellular localization of Gram-positive bacterial proteins with both single and multiple location sites. Rigorous cross-validation on a same stringent benchmark dataset indicated that the proposed pLoc-mGpos predictor is remarkably superior to "iLoc-Gpos", the state-of-the-art predictor for the same purpose. To maximize the convenience of most experimental scientists, a user-friendly web-server for the new powerful predictor has been established at Natural Science http://www.jci-bioinfo.cn/pLoc-mGpos/, by which users can easily get their desired results without the need to go through the complicated mathematics involved.

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Section: Proteins Sample Formulationmentioning

confidence: 99%

pLoc-mGpos: Incorporate Key Gene Ontology Information into General PseAAC for Predicting Subcellular Localization of Gram-Positive Bacterial Proteins

Xiao¹,

Cheng²,

Su³

et al. 2017

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“…Not only protein sequence information but also prediction algorithms could affect the accuracy of subcellular localization prediction (Li et al, 2012). To date, many computational techniques, such as the neural network (Zou et al, 2007), K-nearest neighbor (KNN) (Chou et al, 2006;Xiao et al, 2011b;He et al, 2012), fuzzy KNN (Gu et al, 2010), and Bayesian (Briesemeister et al, 2010a;Simha et al, 2014;Simha et al, 2015), have been introduced for the prediction of protein subcellular localization.…”

Section: Introductionmentioning

confidence: 99%

“…In recent times, a support vector machine (SVM) (Höglund et al, 2006;Li et al, 2012;Wan et al, 2012;Wan et al, 2014;Hasan et al, 2015,) has been extensively applied to provide potential solutions for the prediction of protein subcellular localization. However, the selection of an appropriate kernel and its parameters for a given classification problem influences the performance of the SVM.…”

Section: Introductionmentioning

confidence: 99%

Prediction of protein subcellular localization using support vector machine with the choice of proper kernel

Hasan¹,

Ahmad²,

Molla³

2017

bta

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The prediction of subcellular locations of proteins can provide useful hints for revealing their functions as well as for understanding the mechanisms of some diseases and, finally, for developing novel drugs. As the number of newly discovered proteins has been growing exponentially, laboratory-based experiments to determine the location of an uncharacterized protein in a living cell have become both expensive and time-consuming. Consequently, to tackle these challenges, computational methods are being developed as an alternative to help biologists in selecting target proteins and designing related experiments. However, the success of protein subcellular localization prediction is still a complicated and challenging problem, particularly when query proteins may have multi-label characteristics, i.e. their simultaneous existence in more than one subcellular location, or if they move between two or more different subcellular locations as well. At this point, to get rid of this problem, several types of subcellular localization prediction methods with different levels of accuracy have been proposed. The support vector machine (SVM) has been employed to provide potential solutions for problems connected with the prediction of protein subcellular localization. However, the practicability of SVM is affected by difficulties in selecting its appropriate kernel as well as in selecting the parameters of that selected kernel. The literature survey has shown that most researchers apply the radial basis function (RBF) kernel to build a SVM based subcellular localization prediction system. Surprisingly, there are still many other kernel functions which have not yet been applied in the prediction of protein subcellular localization. However, the nature of this classification problem requires the application of different kernels for SVM to ensure an optimal result. From this viewpoint, this paper presents the work to apply different kernels for SVM in protein subcellular localization prediction to find out which kernel is the best for SVM. We have evaluated our system on a combined dataset containing 5447 single-localized proteins (originally published as part of the Höglund dataset) and 3056 multi-localized proteins (originally published as part of the DBMLoc set). This dataset was used by Briesemeister et al. in their extensive comparison of multilocalization prediction system. The experimental results indicate that the system based on SVM with the Laplace kernel, termed LKLoc, not only achieves a higher accuracy than the system using other kernels but also shows significantly better results than those obtained from other top systems (MDLoc, BNCs, YLoc+). The source code of this prediction system is available upon request.

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“…Recently, several state-of-the-art multi-label predictors have been proposed, such as Hum-mPLoc 2.0 [51], iLoc-Hum [27], mGOASVM [61], HybridGO-Loc [62], R3P-Loc [63], mPLRLoc [64] and other predictors [65,66,67]. They all use the GO information as the features and apply different multi-label classifiers to tackle the multi-label classification problem.…”

Section: Introductionmentioning

confidence: 99%

mLASSO-Hum: A LASSO-based interpretable human-protein subcellular localization predictor

Wan

Mak

Kung

2015

Journal of Theoretical Biology

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An Ensemble Classifier for Eukaryotic Protein Subcellular Location Prediction Using Gene Ontology Categories and Amino Acid Hydrophobicity

Cited by 61 publications

References 61 publications

pLoc-mGpos: Incorporate Key Gene Ontology Information into General PseAAC for Predicting Subcellular Localization of Gram-Positive Bacterial Proteins

pLoc-mGpos: Incorporate Key Gene Ontology Information into General PseAAC for Predicting Subcellular Localization of Gram-Positive Bacterial Proteins

Prediction of protein subcellular localization using support vector machine with the choice of proper kernel

mLASSO-Hum: A LASSO-based interpretable human-protein subcellular localization predictor

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