Identifying subcellular localizations of mammalian protein complexes based on graph theory with a random forest algorithm

Zou, Xiaoyong; Lai, Yan-Hua; Chen, Lili; Chen, Chao; Xie, Yuanfu; Dai, Zong; Zou, Xiaoyong

doi:10.1039/c3mb25451h

Cited by 7 publications

(4 citation statements)

References 30 publications

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“…Some previous work found that most of the nuclear proteins were significantly enriched in protein complexes, and the protein complexes are more likely to have a high clustering coefficient. 74 Based on the above results, we expect that the nuclear proteins would have higher clustering coefficient. As shown in Table 2, the average clustering coefficient of the nuclear proteins is indeed higher than those of other seven categories, and the difference between them is significant (P-value o 2.20 Â 10 À16 ; KW test).…”

Section: Analysis Of Topological Propertiesmentioning

confidence: 88%

Characterization of proteins in S. cerevisiae with subcellular localizations

et al. 2015

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Acquiring comprehensive knowledge of protein in various subcellular localizations is one of the fundamental goals in cell biology and proteomics. Although recent large-scale experimental and proteomics studies of S. cerevisiae protein subcellular localizations are archived in various databases, only a few studies use a systems biology approach to characterize S. cerevisiae proteins at a subcellular localization level. Based on the topological properties and biological properties of S. cerevisiae proteins, we have compared, contrasted and analyzed the statistical properties across eight different subcellular localizations. Significant differences are found in all topological properties and biological properties among eight protein categories. Network topology analysis indicates that the nuclear proteins differ from the other seven protein categories, and tend to have the most important topological properties and play an important role in the network, including the highest degree, core number, and betweenness centrality. In the light of the above, we hope these findings presented in this study may provide important help for protein subcellular localization prediction in S. cerevisiae and provide many new insights for understanding the proteins directly from subcellular localizations.

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Section: Analysis Of Topological Propertiesmentioning

confidence: 88%

Characterization of proteins in S. cerevisiae with subcellular localizations

et al. 2015

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“…Because the machine learning method has the ability to train the model and prediction, it is widely used in proteomics. Current mainstream machine learning methods include random forest (RF) [14], naïve Bayes [15,16], decision tree (DT) [17], support vector machine (SVM) [18], extreme gradient boosting (XGBoost) [19], adaptive boosting (AdaBoost) [20], logistic regression (LR) [21], gradient boosting decision tree (GBDT) [22], etc.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Extracellular Matrix Proteins by Fusing Multiple Feature Information, Elastic Net, and Random Forest Algorithm

et al. 2020

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Extracellular matrix (ECM) proteins play an important role in a series of biological processes of cells. The study of ECM proteins is helpful to further comprehend their biological functions. We propose ECMP-RF (extracellular matrix proteins prediction by random forest) to predict ECM proteins. Firstly, the features of the protein sequence are extracted by combining encoding based on grouped weight, pseudo amino-acid composition, pseudo position-specific scoring matrix, a local descriptor, and an autocorrelation descriptor. Secondly, the synthetic minority oversampling technique (SMOTE) algorithm is employed to process the class imbalance data, and the elastic net (EN) is used to reduce the dimension of the feature vectors. Finally, the random forest (RF) classifier is used to predict the ECM proteins. Leave-one-out cross-validation shows that the balanced accuracy of the training and testing datasets is 97.3% and 97.9%, respectively. Compared with other state-of-the-art methods, ECMP-RF is significantly better than other predictors.

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“…At present, the machine learning algorithms for multisite protein subcellular localization include multi-label k-nearest neighbor algorithm (ML-kNN) [16], multi-label support vector machine algorithm (Rank-SVM) [17] and random forest algorithm (RF) [18]. Compared with these algorithms, the proposed algorithm has the following four characteristics.…”

Section: Introductionmentioning

confidence: 99%

Multi-Site Protein Subcellular Localization Based on Deep Convolutional Neural Network

Cong

Liu²,

Chen

et al. 2019

JNB

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Each part of internal structure of cells which is commonly mentioned as subcellular is highly ordered and interconnected has unique functions. The experiments show that deviated protein delivery to the corresponding subcellular causes of human disease. Studies of protein localization can clarify pathogenesis and find treatments. As protein subcellular localization has a very important position in the field of biology, the research in this area is extremely active. Most of the existing protein sub cellular localization methods are more suitable for single-site sub cellular localization. This paper proposed an algorithm based deep convolution neural network which is suit for multi-site protein subcellular localization and the algorithm is implemented on the human protein database to verify and analyze the performance. In order to further improve the classification result of the algorithm, it was combined ensemble learning and features fusion. It can be inferred from experiments that the proposed algorithm is effective in multi-site protein subcellular localization and the overall correct rate of classification is 59.13% which is higher than SAE, SVM and RF. The algorithm proposed in this paper is more uniform and less affected by the number of samples. When the data samples are different, the classification results will have a certain impact, but the overall classification is good. Besides ensemble learning and features fusion are effective for improving classification result.

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Identifying subcellular localizations of mammalian protein complexes based on graph theory with a random forest algorithm

Cited by 7 publications

References 30 publications

Characterization of proteins in S. cerevisiae with subcellular localizations

Characterization of proteins in S. cerevisiae with subcellular localizations

Prediction of Extracellular Matrix Proteins by Fusing Multiple Feature Information, Elastic Net, and Random Forest Algorithm

Multi-Site Protein Subcellular Localization Based on Deep Convolutional Neural Network

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