Construction of reliable protein–protein interaction networks using weighted sparse representation based classifier with pseudo substitution matrix representation features

Huang, Yu An; You, Zhu-Hong; Xiao, Li; Chen, Xing; Hu, Pengwei; Li, Shuai; Luo, Xin

doi:10.1016/j.neucom.2016.08.063

Cited by 43 publications

(15 citation statements)

References 27 publications

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“…Current approaches to predict new links on biological bipartite networks are mainly based on similarity-based assumption (Sun et al, 2018). Given a network in which two types of nodes representing two kinds of research objects are involved, most of previous prediction model assumes that similar objects of one type tend to be associated with those of another type (Huang et al, 2016b). Therefore, their prediction performance could be greatly influenced by the measurement they adopt to calculate the similarity scores among object of the same types (Huang et al, 2017b).…”

Section: Resultsmentioning

confidence: 99%

Predicting lncRNA-miRNA Interaction via Graph Convolution Auto-Encoder

Huang

You

et al. 2019

Front. Genet.

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The interaction of miRNA and lncRNA is known to be important for gene regulations. However, the number of known lncRNA-miRNA interactions is still very limited and there are limited computational tools available for predicting new ones. Considering that lncRNAs and miRNAs share internal patterns in the partnership between each other, the underlying lncRNA-miRNA interactions could be predicted by utilizing the known ones, which could be considered as a semi-supervised learning problem. It is shown that the attributes of lncRNA and miRNA have a close relationship with the interaction between each other. Effective use of side information could be helpful for improving the performance especially when the training samples are limited. In view of this, we proposed an end-to-end prediction model called GCLMI (Graph Convolution for novel lncRNA-miRNA Interactions) by combining the techniques of graph convolution and auto-encoder. Without any preprocessing process on the feature information, our method can incorporate raw data of node attributes with the topology of the interaction network. Based on a real dataset collected from a public database, the results of experiments conducted on k-fold cross validations illustrate the robustness and effectiveness of the prediction performance of the proposed prediction model. We prove the graph convolution layer as designed in the proposed model able to effectively integrate the input data by filtering the graph with node features. The proposed model is anticipated to yield highly potential lncRNA-miRNA interactions in the scenario that different types of numerical features describing lncRNA or miRNA are provided by users, serving as a useful computational tool.

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Section: Resultsmentioning

confidence: 99%

Predicting lncRNA-miRNA Interaction via Graph Convolution Auto-Encoder

Huang

You

et al. 2019

Front. Genet.

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“…Table 5 describes the average accuracies of other seven methods including LDA+RF (Xiao-Yong et al, 2010), LDA+RoF (Xiao-Yong et al, 2010), AC+RF (Xiao-Yong et al, 2010), AC+RoF [41), WSRC+GE (Huang et al, 2016a), and HOG+SVD+RF (Ding et al, 2016). Table 6 describes the average accuracies of other seven methods including AutoCC (Yanzhi et al, 2008), SVM+LD (Guo et al, 2015), RF+PR+LPQ (Wong et al, 2015), PCA+ELM (You et al, 2013), WSRC+PSM (Huang et al, 2016b), HOG+SVD+RF (Ding et al, 2016), and RVM+BiGP (An et al, 2016). These results using distinct methods on three datasets are intuitively shown by Figure 5B.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

FCTP-WSRC: Protein–Protein Interactions Prediction via Weighted Sparse Representation Based Classification

Kong

Zhang

et al. 2020

Front. Genet.

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The task of predicting protein-protein interactions (PPIs) has been essential in the context of understanding biological processes. This paper proposes a novel computational model namely FCTP-WSRC to predict PPIs effectively. Initially, combinations of the F-vector, composition (C) and transition (T) are used to map each protein sequence onto numeric feature vectors. Afterwards, an effective feature extraction method PCA (principal component analysis) is employed to reconstruct the most discriminative feature subspaces, which is subsequently used as input in weighted sparse representation based classification (WSRC) for prediction. The FCTP-WSRC model achieves accuracies of 96.67%, 99.82%, and 98.09% for H. pylori, Human and Yeast datasets respectively. Furthermore, the FCTP-WSRC model performs well when predicting three significant PPIs networks: the single-core network (CD9), the multiple-core network (Ras-Raf-Mek-Erk-Elk-Srf pathway), and the cross-connection network (Wnt-related Network). Consequently, the promising results show that the proposed method can be a powerful tool for PPIs prediction with excellent performance and less time.

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“…Currently, many kinds of computational models based on protein sequences have been presented for predicting PPIs. In this section, to further objectively validate the prediction performance of the proposed method, seven state-of-the-art methods, including Ensemble Deep Neural Networks (EnsDNN) [22], 3-mers-based [31], Bio2vec-based [31], pseudo Substitution Matrix Representation (pseudo-SMR) [32], WSRC with continuous wavelet and discrete wavelet transform (WSRC+CW and DW) [33], feature weighted rotation forest algorithm (FWRF) [17], and Global encoding [34] were compared on the human, H. pylori, and yeast data sets. The comparison results of three benchmark data sets based on five-fold cross-validation of different models are plotted in Figures 2-4, respectively.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

Protein-Protein Interactions Prediction Based on Graph Energy and Protein Sequence Information

Xu¹,

Xu²,

Zhang³

et al. 2020

Molecules

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Identification of protein-protein interactions (PPIs) plays an essential role in the understanding of protein functions and cellular biological activities. However, the traditional experiment-based methods are time-consuming and laborious. Therefore, developing new reliable computational approaches has great practical significance for the identification of PPIs. In this paper, a novel prediction method is proposed for predicting PPIs using graph energy, named PPI-GE. Particularly, in the process of feature extraction, we designed two new feature extraction methods, the physicochemical graph energy based on the ionization equilibrium constant and isoelectric point and the contact graph energy based on the contact information of amino acids. The dipeptide composition method was used for order information of amino acids. After multi-information fusion, principal component analysis (PCA) was implemented for eliminating noise and a robust weighted sparse representation-based classification (WSRC) classifier was applied for sample classification. The prediction accuracies based on the five-fold cross-validation of the human, Helicobacter pylori (H. pylori), and yeast data sets were 99.49%, 97.15%, and 99.56%, respectively. In addition, in five independent data sets and two significant PPI networks, the comparative experimental results also demonstrate that PPI-GE obtained better performance than the compared methods.

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Construction of reliable protein–protein interaction networks using weighted sparse representation based classifier with pseudo substitution matrix representation features

Cited by 43 publications

References 27 publications

Predicting lncRNA-miRNA Interaction via Graph Convolution Auto-Encoder

Predicting lncRNA-miRNA Interaction via Graph Convolution Auto-Encoder

FCTP-WSRC: Protein–Protein Interactions Prediction via Weighted Sparse Representation Based Classification

Protein-Protein Interactions Prediction Based on Graph Energy and Protein Sequence Information

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