Prediction of Apoptosis Proteins Subcellular Location Using Evolutionary Profiles and Motifs Information

Apoptosis proteins are very important for regulating the balance between cell proliferation and death. Because the function of apoptosis protein is closely related to its subcellular location, it is desirable to explore their function by predicting the subcellular location of apoptosis protein. In this paper, based on evolutionary profiles and motifs information of protein sequences, an approach for predicting apoptosis proteins subcellular location is presented by using support vector machine (SVM). When the method is applied to three data sets (98 apoptosis proteins dataset, 225 apoptosis proteins dataset and 317 apoptosis proteins dataset), the overall accuracies of our method on the three data sets reach 95.9%, 86.7% and 91.8% in the jackknife test, respectively. The higher predictive success rates indicate that the proposed method is very useful for apoptosis proteins subcellular localization.

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Predicting Apoptosis Protein Subcellular Locations based on the Protein Overlapping Property Matrix and Tri-Gram Encoding

International Journal of Molecular Sciences ◽

10.3390/ijms20092344 ◽

2019 ◽

Vol 20 (9) ◽

pp. 2344

Author(s):

Yang Yang ◽

Huiwen Zheng ◽

Chunhua Wang ◽

Wanyue Xiao ◽

Taigang Liu

Keyword(s):

Support Vector Machine ◽

Subcellular Location ◽

Recursive Feature Elimination ◽

Support Vector ◽

Svm Classifier ◽

Protein Subcellular Location ◽

Promising Tool ◽

Apoptosis Protein ◽

Benchmark Datasets ◽

Apoptosis Proteins

To reveal the working pattern of programmed cell death, knowledge of the subcellular location of apoptosis proteins is essential. Besides the costly and time-consuming method of experimental determination, research into computational locating schemes, focusing mainly on the innovation of representation techniques on protein sequences and the selection of classification algorithms, has become popular in recent decades. In this study, a novel tri-gram encoding model is proposed, which is based on using the protein overlapping property matrix (POPM) for predicting apoptosis protein subcellular location. Next, a 1000-dimensional feature vector is built to represent a protein. Finally, with the help of support vector machine-recursive feature elimination (SVM-RFE), we select the optimal features and put them into a support vector machine (SVM) classifier for predictions. The results of jackknife tests on two benchmark datasets demonstrate that our proposed method can achieve satisfactory prediction performance level with less computing capacity required and could work as a promising tool to predict the subcellular locations of apoptosis proteins.

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Prediction of Subcellular Location for Apoptosis Proteins by Dual-layer Support Vector Machine Based on Multiple Compositions

2007 1st International Conference on Bioinformatics and Biomedical Engineering ◽

10.1109/icbbe.2007.28 ◽

2007 ◽

Cited By ~ 2

Author(s):

Xi-Bin Zhou ◽

Chao Chen ◽

Zhan-Chao Li ◽

Xiao-Yong Zou

Keyword(s):

Support Vector Machine ◽

Subcellular Location ◽

Support Vector ◽

Apoptosis Proteins

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MultiP-Apo: A Multilabel Predictor for Identifying Subcellular Locations of Apoptosis Proteins

Computational Intelligence and Neuroscience ◽

10.1155/2017/9183796 ◽

2017 ◽

Vol 2017 ◽

pp. 1-10 ◽

Cited By ~ 3

Author(s):

Xiao Wang ◽

Hui Li ◽

Rong Wang ◽

Qiuwen Zhang ◽

Weiwei Zhang ◽

...

Keyword(s):

Subcellular Localization ◽

Prediction Models ◽

Feature Vector ◽

Subcellular Location ◽

Computational Prediction ◽

Initial Study ◽

Feature Extraction Method ◽

Jackknife Test ◽

Apoptosis Protein ◽

Apoptosis Proteins

Apoptosis proteins play an important role in the mechanism of programmed cell death. Predicting subcellular localization of apoptosis proteins is an essential step to understand their functions and identify drugs target. Many computational prediction methods have been developed for apoptosis protein subcellular localization. However, these existing works only focus on the proteins that have one location; proteins with multiple locations are either not considered or assumed as not existing when constructing prediction models, so that they cannot completely predict all the locations of the apoptosis proteins with multiple locations. To address this problem, this paper proposes a novel multilabel predictor named MultiP-Apo, which can predict not only apoptosis proteins with single subcellular location but also those with multiple subcellular locations. Specifically, given a query protein, GO-based feature extraction method is used to extract its feature vector. Subsequently, the GO feature vector is classified by a new multilabel classifier based on the label-specific features. It is the first multilabel predictor ever established for identifying subcellular locations of multilocation apoptosis proteins. As an initial study, MultiP-Apo achieves an overall accuracy of 58.49% by jackknife test, which indicates that our proposed predictor may become a very useful high-throughput tool in this area.

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Improved prediction of subcellular location for apoptosis proteins by the dual-layer support vector machine

Amino Acids ◽

10.1007/s00726-007-0608-y ◽

2007 ◽

Vol 35 (2) ◽

pp. 383-388 ◽

Cited By ~ 38

Author(s):

X.-B. Zhou ◽

C. Chen ◽

Z.-C. Li ◽

X.-Y. Zou

Keyword(s):

Support Vector Machine ◽

Subcellular Location ◽

Support Vector ◽

Apoptosis Proteins

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Predicting subcellular location of apoptosis proteins based on wavelet transform and support vector machine

Amino Acids ◽

10.1007/s00726-009-0331-y ◽

2009 ◽

Vol 38 (4) ◽

pp. 1201-1208 ◽

Cited By ~ 27

Author(s):

Jian-Ding Qiu ◽

San-Hua Luo ◽

Jian-Hua Huang ◽

Xing-Yu Sun ◽

Ru-Ping Liang

Keyword(s):

Support Vector Machine ◽

Wavelet Transform ◽

Subcellular Location ◽

Support Vector ◽

Apoptosis Proteins

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

BioMed Research International ◽

10.1155/2019/2436924 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 1

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

Current Bioinformatics ◽

10.2174/1574893614666190902155811 ◽

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.

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A One-shot Learning Approach to Image Classification using Genetic Programming

10.26686/wgtn.13150934.v1 ◽

2020 ◽

Author(s):

Harith Al-Sahaf ◽

Mengjie Zhang ◽

M Johnston

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Genetic Programming ◽

Image Classification ◽

Local Binary Patterns ◽

Support Vector ◽

Learning Approach ◽

Data Sets ◽

Domain Specific ◽

International Publishing

In machine learning, it is common to require a large number of instances to train a model for classification. In many cases, it is hard or expensive to acquire a large number of instances. In this paper, we propose a novel genetic programming (GP) based method to the problem of automatic image classification via adopting a one-shot learning approach. The proposed method relies on the combination of GP and Local Binary Patterns (LBP) techniques to detect a predefined number of informative regions that aim at maximising the between-class scatter and minimising the within-class scatter. Moreover, the proposed method uses only two instances of each class to evolve a classifier. To test the effectiveness of the proposed method, four different texture data sets are used and the performance is compared against two other GP-based methods namely Conventional GP and Two-tier GP. The experiments revealed that the proposed method outperforms these two methods on all the data sets. Moreover, a better performance has been achieved by Naïve Bayes, Support Vector Machine, and Decision Trees (J48) methods when extracted features by the proposed method have been used compared to the use of domain-specific and Two-tier GP extracted features. © Springer International Publishing 2013.

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