Prediction of subcellular location of apoptosis proteins by incorporating PsePSSM and DCCA coefficient based on LFDA dimensionality reduction

Yu, Bin; Li, Shan; Qiu, Wenying; Wang, Minghui; Du, Junwei; Zhang, Yusen; Chen, Xing

doi:10.1186/s12864-018-4849-9

Cited by 58 publications

(20 citation statements)

References 94 publications

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“…With a large number of protein sequences in the public domain, the traditional biological experiments are difficult to meet the demands in the PPI research field. The critical challenge of bioinformatics is to develop computational methods for efficiently and accurately determining the structures and functions of proteins (Afolabi et al, 2018;Lei et al, 2016Lei et al, , 2018Song et al, 2017;Wang et al, 2017;Yu et al, 2018). In this study, we presented a machine learning method EL-SMURF to predict the PPI sites from protein sequences, whose prediction accuracies achieve 79.1%, 77.1% and 77.7%, respectively, on the datasets Dest186, Dtestset72 and PDBtestset164.…”

Section: Resultsmentioning

confidence: 99%

Protein–protein interaction sites prediction by ensemble random forests with synthetic minority oversampling technique

Wang

et al. 2018

Bioinformatics

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135

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Motivation The prediction of protein–protein interaction (PPI) sites is a key to mutation design, catalytic reaction and the reconstruction of PPI networks. It is a challenging task considering the significant abundant sequences and the imbalance issue in samples. Results A new ensemble learning-based method, Ensemble Learning of synthetic minority oversampling technique (SMOTE) for Unbalancing samples and RF algorithm (EL-SMURF), was proposed for PPI sites prediction in this study. The sequence profile feature and the residue evolution rates were combined for feature extraction of neighboring residues using a sliding window, and the SMOTE was applied to oversample interface residues in the feature space for the imbalance problem. The Multi-dimensional Scaling feature selection method was implemented to reduce feature redundancy and subset selection. Finally, the Random Forest classifiers were applied to build the ensemble learning model, and the optimal feature vectors were inserted into EL-SMURF to predict PPI sites. The performance validation of EL-SMURF on two independent validation datasets showed 77.1% and 77.7% accuracy, which were 6.2–15.7% and 6.1–18.9% higher than the other existing tools, respectively. Availability and implementation The source codes and data used in this study are publicly available at http://github.com/QUST-AIBBDRC/EL-SMURF/. Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Resultsmentioning

confidence: 99%

Protein–protein interaction sites prediction by ensemble random forests with synthetic minority oversampling technique

Wang

et al. 2018

Bioinformatics

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135

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“…Pseudo position-specific scoring matrix (PsePSSM) proposed by Chou and Shen [17] is widely used in proteomics prediction [18][19][20][21]. We use the PSI-BLAST program [22] to perform three iterative searches with E value of 0.001 for UniProtKB / Swiss-Prot database.…”

Section: Pseudo Position-specific Scoring Matrixmentioning

confidence: 99%

Fertility-LightGBM: A fertility-related protein prediction model by multi-information fusion and light gradient boosting machine

Yue

Wang

Yang

et al. 2020

Preprint

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The identification of fertility-related proteins plays an essential part in understanding the embryogenesis of germ cell development. Since the traditional experimental methods are expensive and time-consuming to identify fertility-related proteins, the purposes of predicting protein functions from amino acid sequences appeared. In this paper, we propose a fertility-related protein prediction model. Firstly, the model combines protein physicochemical property information, evolutionary information and sequence information to construct the initial feature space ‘ALL’. Then, the least absolute shrinkage and selection operator (LASSO) is used to remove redundant features. Finally, light gradient boosting machine (LightGBM) is used as a classifier to predict. The 5-fold cross-validation accuracy of the training dataset is 88.5%, and the independent accuracy of the training dataset is 91.5%. The results show that our model is more competitive for the prediction of fertility-related proteins, which is helpful for the study of fertility diseases and related drug targets.

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“…Tang et al [37] proposed a method named iAPSL-IF to identify the subcelular location of apoptosis protein using the SVM-RFE feature selection method. Yu et al [38] proposed a model for the prediction of subcellular location of apoptosis proteins and in their work, local fisher discriminant analysis (LFDA) was employed to reduce the dimension of the features. Wang et al [2] considered four global algorithms of dimensional reduction, including linear discriminant analysis (LDA), median LDA (MDA), generalized Fisher discriminant analysis (GDA), and median-mean line-based discriminant analysis (MMLDA) to map the high-dimensional data into a low-dimensional spaces.…”

Section: Introductionmentioning

confidence: 99%

Using Evolutionary Information and Multi-Label Linear Discriminant Analysis to Predict the Subcellular Location of Multi-Site Bacterial Proteins via Chou’s 5-Steps Rule

Du¹,

Meng²,

Jiang³

et al. 2020

IEEE Access

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The function of a protein is closely tied to its subcellular location. Identifying the subcellular location of proteins is a crucial step to understand their functions. However, determining the subcellular location of proteins experimentally is time-consuming and costly. Therefore, developing effective computational methods to predict the subcellular positions of proteins is a hotspot in bioinformatics. Though many models have been proposed to improve the prediction accuracy of protein subcellular localization, there are still several shortcomings: (1) numerous methods ignore the multi-site proteins; (2) high dimensional features bring the burden to the construction of the prediction model. In this work, we proposed a method to predict the subcellular location of bacterial proteins with both single and multiple locations. Two features based on evolutionary information are extracted to solve the multi-site prediction problem, of which one is a 190-dimensional feature vector from absolute entropy correlation analysis (AECA-PSSM) and another is a 480-dimensional feature vector extracted using discrete wavelet transform (PSSM-DWT). After combining both proposed features, multi-label linear discriminant analysis (MLDA) is employed to transform the high-dimensional feature space into a lower-dimensional space. Multi-label k-nearest neighbors algorithm (ML-KNN) is utilized to predict the subcellular location of both single-site and multi-site proteins. Experimental results on Gram-positive dataset and Gram-negative dataset show the effectiveness of the proposed method. INDEX TERMS Subcellular location, absolute entropy correlation analysis, discrete wavelet transform, multi-label linear discriminant analysis, multi-label k-nearest neighbors.

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Prediction of subcellular location of apoptosis proteins by incorporating PsePSSM and DCCA coefficient based on LFDA dimensionality reduction

Cited by 58 publications

References 94 publications

Protein–protein interaction sites prediction by ensemble random forests with synthetic minority oversampling technique

Protein–protein interaction sites prediction by ensemble random forests with synthetic minority oversampling technique

Fertility-LightGBM: A fertility-related protein prediction model by multi-information fusion and light gradient boosting machine

Using Evolutionary Information and Multi-Label Linear Discriminant Analysis to Predict the Subcellular Location of Multi-Site Bacterial Proteins via Chou’s 5-Steps Rule

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