Detrended cross-correlation coefficient: Application to predict apoptosis protein subcellular localization

2016 ◽  
Vol 282 ◽  
pp. 61-67 ◽  
Author(s):  
Yunyun Liang ◽  
Sanyang Liu ◽  
Shengli Zhang
Keyword(s):  
Subcellular Localization ◽  
Correlation Coefficient ◽  
Cross Correlation ◽  
Protein Subcellular Localization ◽  
Apoptosis Protein

Integrating Second-order Moving Average and Over-sampling Algorithm to Predict Apoptosis Protein Subcellular Localization

2020 ◽  
Vol 15 (6) ◽  
pp. 517-527
Author(s):  
Yunyun Liang ◽  
Shengli Zhang
Keyword(s):  
Subcellular Localization ◽  
Moving Average ◽  
Subcellular Location ◽  
Second Order ◽  
Test Method ◽  
Support Vector ◽  
Protein Subcellular Localization ◽  
Protein Subcellular Location ◽  
Apoptosis Protein ◽  
Leibler Divergence

Background: Apoptosis proteins have a key role in the development and the homeostasis of the organism, and are very important to understand the mechanism of cell proliferation and death. The function of apoptosis protein is closely related to its subcellular location. Objective: Prediction of apoptosis protein subcellular localization is a meaningful task. Methods: In this study, we predict the apoptosis protein subcellular location by using the PSSMbased second-order moving average descriptor, nonnegative matrix factorization based on Kullback-Leibler divergence and over-sampling algorithms. This model is named by SOMAPKLNMF- OS and constructed on the ZD98, ZW225 and CL317 benchmark datasets. Then, the support vector machine is adopted as the classifier, and the bias-free jackknife test method is used to evaluate the accuracy. Results: Our prediction system achieves the favorable and promising performance of the overall accuracy on the three datasets and also outperforms the other listed models. Conclusion: The results show that our model offers a high throughput tool for the identification of apoptosis protein subcellular localization.

scholarly journals Identification of Protein Subcellular Localization With Network and Functional Embeddings

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiaoyong Pan ◽  
Hao Li ◽  
Tao Zeng ◽  
Zhandong Li ◽  
Lei Chen ◽  
...  
Keyword(s):  
Subcellular Localization ◽  
Correlation Coefficient ◽  
Computational Methods ◽  
Matthews Correlation Coefficient ◽  
Benchmark Dataset ◽  
Protein Network ◽  
Classification Model ◽  
Protein Subcellular Localization ◽  
Conventional Methods ◽  
Go Terms

The functions of proteins are mainly determined by their subcellular localizations in cells. Currently, many computational methods for predicting the subcellular localization of proteins have been proposed. However, these methods require further improvement, especially when used in protein representations. In this study, we present an embedding-based method for predicting the subcellular localization of proteins. We first learn the functional embeddings of KEGG/GO terms, which are further used in representing proteins. Then, we characterize the network embeddings of proteins on a protein–protein network. The functional and network embeddings are combined as novel representations of protein locations for the construction of the final classification model. In our collected benchmark dataset with 4,861 proteins from 16 locations, the best model shows a Matthews correlation coefficient of 0.872 and is thus superior to multiple conventional methods.

scholarly journals Prediction of Apoptosis Protein Subcellular Localization with Multilayer Sparse Coding and Oversampling Approach

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Xingjian Chen ◽  
Xuejiao Hu ◽  
Wenxin Yi ◽  
Xiang Zou ◽  
Wei Xue
Keyword(s):  
Subcellular Localization ◽  
Sparse Coding ◽  
Sequence Data ◽  
Support Vector ◽  
Data Sets ◽  
Protein Subcellular Localization ◽  
Jackknife Test ◽  
Apoptosis Protein ◽  
Extraction Algorithm ◽  
Protein Subcellular Localization Prediction

The prediction of apoptosis protein subcellular localization plays an important role in understanding the progress in cell proliferation and death. Recently computational approaches to this issue have become very popular, since the traditional biological experiments are so costly and time-consuming that they cannot catch up with the growth rate of sequence data anymore. In order to improve the prediction accuracy of apoptosis protein subcellular localization, we proposed a sparse coding method combined with traditional feature extraction algorithm to complete the sparse representation of apoptosis protein sequences, using multilayer pooling based on different sizes of dictionaries to integrate the processed features, as well as oversampling approach to decrease the influences caused by unbalanced data sets. Then the extracted features were input to a support vector machine to predict the subcellular localization of the apoptosis protein. The experiment results obtained by Jackknife test on two benchmark data sets indicate that our method can significantly improve the accuracy of the apoptosis protein subcellular localization prediction.

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