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

Xu, Da; Xu, Hanxiao; Zhang, Yusen; Chen, Wei; Gao, Rui

doi:10.3390/molecules25081841

Cited by 11 publications

(11 citation statements)

References 48 publications

(65 reference statements)

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“…After achieving a lower-dimensional representation of the data by feature selection, we adopted SVM (use RBF kernel) and kNN (k = 5) classifiers to classify the data, respectively. The cross-validation is a popular evaluation method and has been widely used in the field of bioinformatics and related studies [ 8 , 16 , 33 ]. We performed 10-fold cross-validation for 10 times to obtain a statistically reliable predictive performance.…”

Section: Resultsmentioning

confidence: 99%

Multi-scale supervised clustering-based feature selection for tumor classification and identification of biomarkers and targets on genomic data

Zhang

et al. 2020

BMC Genomics

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Background The small number of samples and the curse of dimensionality hamper the better application of deep learning techniques for disease classification. Additionally, the performance of clustering-based feature selection algorithms is still far from being satisfactory due to their limitation in using unsupervised learning methods. To enhance interpretability and overcome this problem, we developed a novel feature selection algorithm. In the meantime, complex genomic data brought great challenges for the identification of biomarkers and therapeutic targets. The current some feature selection methods have the problem of low sensitivity and specificity in this field. Results In this article, we designed a multi-scale clustering-based feature selection algorithm named MCBFS which simultaneously performs feature selection and model learning for genomic data analysis. The experimental results demonstrated that MCBFS is robust and effective by comparing it with seven benchmark and six state-of-the-art supervised methods on eight data sets. The visualization results and the statistical test showed that MCBFS can capture the informative genes and improve the interpretability and visualization of tumor gene expression and single-cell sequencing data. Additionally, we developed a general framework named McbfsNW using gene expression data and protein interaction data to identify robust biomarkers and therapeutic targets for diagnosis and therapy of diseases. The framework incorporates the MCBFS algorithm, network recognition ensemble algorithm and feature selection wrapper. McbfsNW has been applied to the lung adenocarcinoma (LUAD) data sets. The preliminary results demonstrated that higher prediction results can be attained by identified biomarkers on the independent LUAD data set, and we also structured a drug-target network which may be good for LUAD therapy. Conclusions The proposed novel feature selection method is robust and effective for gene selection, classification, and visualization. The framework McbfsNW is practical and helpful for the identification of biomarkers and targets on genomic data. It is believed that the same methods and principles are extensible and applicable to other different kinds of data sets.

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Section: Resultsmentioning

confidence: 99%

Multi-scale supervised clustering-based feature selection for tumor classification and identification of biomarkers and targets on genomic data

Zhang

et al. 2020

BMC Genomics

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“… Zhao et al (2020) predict PPIs that combined the spatial relationship of protein sequence with the potential sequential feature of the ontological annotation semantics. Xu et al (2020) developed a method called PPI-GE, which predicts PPIs by combining the contact graph energy and physicochemical graph energy. Xiao and Deng (2020) proposed a new node embedding approach to predict PPIs that captures the topological information from higher-order neighborhoods of PPI network nodes.…”

Section: Introductionmentioning

confidence: 99%

DWPPI: A Deep Learning Approach for Predicting Protein–Protein Interactions in Plants Based on Multi-Source Information With a Large-Scale Biological Network

Pan

You

et al. 2022

Front. Bioeng. Biotechnol.

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The prediction of protein–protein interactions (PPIs) in plants is vital for probing the cell function. Although multiple high-throughput approaches in the biological domain have been developed to identify PPIs, with the increasing complexity of PPI network, these methods fall into laborious and time-consuming situations. Thus, it is essential to develop an effective and feasible computational method for the prediction of PPIs in plants. In this study, we present a network embedding-based method, called DWPPI, for predicting the interactions between different plant proteins based on multi-source information and combined with deep neural networks (DNN). The DWPPI model fuses the protein natural language sequence information (attribute information) and protein behavior information to represent plant proteins as feature vectors and finally sends these features to a deep learning–based classifier for prediction. To validate the prediction performance of DWPPI, we performed it on three model plant datasets: Arabidopsis thaliana (A. thaliana), mazie (Zea mays), and rice (Oryza sativa). The experimental results with the fivefold cross-validation technique demonstrated that DWPPI obtains great performance with the AUC (area under ROC curves) values of 0.9548, 0.9867, and 0.9213, respectively. To further verify the predictive capacity of DWPPI, we compared it with some different state-of-the-art machine learning classifiers. Moreover, case studies were performed with the AC149810.2_FGP003 protein. As a result, 14 of the top 20 PPI pairs identified by DWPPI with the highest scores were confirmed by the literature. These excellent results suggest that the DWPPI model can act as a promising tool for related plant molecular biology.

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“…Because of the known disease-related microbes being insufficient, developing effective computational methods is necessary for reducing the cost and time of biological experiments. Recently, with the deepening of studies on computational biology, many computation-based methods have been proposed and achieved successful applications in the bioinformatics field, such as miRNA–disease ( Peng et al, 2018a ; Chen et al, 2019 ) or drug–target ( Chen et al, 2016 ) association prediction, and lncRNA–miRNA ( Zhang et al, 2021 ), protein–protein ( Xu et al, 2020a ), or lncRNA–protein ( Peng et al, 2021 ; Zhou et al, 2021 ) interaction prediction.…”

Section: Introductionmentioning

confidence: 99%

Novel Collaborative Weighted Non-negative Matrix Factorization Improves Prediction of Disease-Associated Human Microbes

Zhang

et al. 2022

Front. Microbiol.

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Extensive clinical and biomedical studies have shown that microbiome plays a prominent role in human health. Identifying potential microbe–disease associations (MDAs) can help reveal the pathological mechanism of human diseases and be useful for the prevention, diagnosis, and treatment of human diseases. Therefore, it is necessary to develop effective computational models and reduce the cost and time of biological experiments. Here, we developed a novel machine learning-based joint framework called CWNMF-GLapRLS for human MDA prediction using the proposed collaborative weighted non-negative matrix factorization (CWNMF) technique and graph Laplacian regularized least squares. Especially, to fuse more similarity information, we calculated the functional similarity of microbes. To deal with missing values and effectively overcome the data sparsity problem, we proposed a collaborative weighted NMF technique to reconstruct the original association matrix. In addition, we developed a graph Laplacian regularized least-squares method for prediction. The experimental results of fivefold and leave-one-out cross-validation demonstrated that our method achieved the best performance by comparing it with 5 state-of-the-art methods on the benchmark dataset. Case studies further showed that the proposed method is an effective tool to predict potential MDAs and can provide more help for biomedical researchers.

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

Cited by 11 publications

References 48 publications

Multi-scale supervised clustering-based feature selection for tumor classification and identification of biomarkers and targets on genomic data

Multi-scale supervised clustering-based feature selection for tumor classification and identification of biomarkers and targets on genomic data

DWPPI: A Deep Learning Approach for Predicting Protein–Protein Interactions in Plants Based on Multi-Source Information With a Large-Scale Biological Network

Novel Collaborative Weighted Non-negative Matrix Factorization Improves Prediction of Disease-Associated Human Microbes

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