A novel essential protein identification method based on PPI networks and gene expression data

Zhong, Jiancheng; Tang, Chao; Peng, Wei; Xie, Minzhu; Sun, Yusui; Tang, Qiang; Xiao, Qiu; Yang, Jiahong

doi:10.1186/s12859-021-04175-8

Cited by 43 publications

(28 citation statements)

References 49 publications

(35 reference statements)

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“…The PIN for yeast contains 1167 essential proteins (EP) gathered from MIPS, 31 SGD, 32 DEG, 33 and SGDP 34 . Zhong et al 35 proposed a strategy named JDC for identifying EPs based on PIN and GE data. Statistics are produced to define the active proteins that are essential.…”

Section: Resultsmentioning

confidence: 99%

“…The PIN for yeast contains 1167 essential proteins (EP) gathered from MIPS, 31 SGD, 32 DEG, 33 and SGDP. 34 Zhong et al 35 {"YML051W," "YDR009W"} {"YML051W," "YDR009W"} 2 3 { "YML051W," "YPL248C"} {"YML051W," "YPL248C"} 14 {"YPL046C," "'YJR052W"} {"YBR114W," "YPL046C," "YJR052W," "YKL112W"} 15 {"YPL046C," "YNL230C"} {"YPL046C," "YNL230C"} Abbreviation: PIN, protein interaction network; PPC, predicted protein complex; RPC, reference protein complex.…”

Section: Dpins Analysismentioning

confidence: 99%

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Construction of dynamic protein interaction network based on gene expression data and quartile one principle

2022

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Real protein interaction network (PIN) is dynamic. Researchers created dynamic PIN by combining static PIN with gene expression data to explain the dynamicity evolution of protein interactions. However, all available approaches failed to recognize low-or high-expression proteins as active proteins. Therefore, determining an adequate threshold is one of the system's biological challenges. In this study, a quartile one (q-one) method is proposed to determine the active time points for each protein according to its expression value's features and construct dynamic protein interaction networks (DPINs) in which a protein is added to the network if it is active in two successive time points. This leads to reduce the number of DPINs to half. The efficiency of the q-one approach and three-sigma (3-sigma) method in detecting protein complexes is evaluated using Markov cluster method, clique percolation method, and ClusterONE algorithms. In most cases, q-one outperforms the 3-sigma method in recall, precision and F-measure. This is in addition to its ability to reveal the dynamicity within the protein-protein interaction network and identify essential proteins.

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Section: Resultsmentioning

confidence: 99%

Section: Dpins Analysismentioning

confidence: 99%

Construction of dynamic protein interaction network based on gene expression data and quartile one principle

2022

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“…This provides scope for developing predictive models for identifying essential genes using genome-wide data by applying advanced computational techniques such as machine learning on genome-wide association studies (GWAS), e.g., EssRank (Xu et al 2019 ). Integration of gene expression data and network topological features has been employed for predicting gene essentiality (Zhong et al 2021 ).…”

Section: Applications Of Emergent Machine Learning-based Approaches I...mentioning

confidence: 99%

Emerging landscape of molecular interaction networks: Opportunities, challenges and prospects

et al. 2022

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Network biology finds application in interpreting molecular interaction networks and providing insightful inferences using graph theoretical analysis of biological systems. The integration of computational bio-modelling approaches with different hybrid network-based techniques provides additional information about the behaviour of complex systems. With increasing advances in high-throughput technologies in biological research, attempts have been made to incorporate this information into network structures, which has led to a continuous update of network biology approaches over time. The newly minted centrality measures accommodate the details of omics data and regulatory network structure information. The unification of graph network properties with classical mathematical and computational modelling approaches and technologically advanced approaches like machine-learning- and artificial intelligence-based algorithms leverages the potential application of these techniques. These computational advances prove beneficial and serve various applications such as essential gene prediction, identification of drug–disease interaction and gene prioritization. Hence, in this review, we have provided a comprehensive overview of the emerging landscape of molecular interaction networks using graph theoretical approaches. With the aim to provide information on the wide range of applications of network biology approaches in understanding the interaction and regulation of genes, proteins, enzymes and metabolites at different molecular levels, we have reviewed the methods that utilize network topological properties, emerging hybrid network-based approaches and applications that integrate machine learning techniques to analyse molecular interaction networks. Further, we have discussed the applications of these approaches in biomedical research with a note on future prospects.

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“…Another method, WDC [ 13 ], units the Pearson correlation coefficient to measure the gene expression profiles and the edge clustering coefficient to weigh the PPI network to identify the essential proteins. Lately, JDC [ 14 ] refers to the fluctuations problem in gene expression data and offers a dynamic threshold method to binarize it, then combines the degree centrality and Jaccard similarity index to calculate the JDC score for each protein in the PPI network. Furthermore, studies on the construction of the PPI network have emerged.…”

Section: Introductionmentioning

confidence: 99%

A deep learning framework for identifying essential proteins based on multiple biological information

et al. 2022

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Background Essential Proteins are demonstrated to exert vital functions on cellular processes and are indispensable for the survival and reproduction of the organism. Traditional centrality methods perform poorly on complex protein–protein interaction (PPI) networks. Machine learning approaches based on high-throughput data lack the exploitation of the temporal and spatial dimensions of biological information. Results We put forward a deep learning framework to predict essential proteins by integrating features obtained from the PPI network, subcellular localization, and gene expression profiles. In our model, the node2vec method is applied to learn continuous feature representations for proteins in the PPI network, which capture the diversity of connectivity patterns in the network. The concept of depthwise separable convolution is employed on gene expression profiles to extract properties and observe the trends of gene expression over time under different experimental conditions. Subcellular localization information is mapped into a long one-dimensional vector to capture its characteristics. Additionally, we use a sampling method to mitigate the impact of imbalanced learning when training the model. With experiments carried out on the data of Saccharomyces cerevisiae, results show that our model outperforms traditional centrality methods and machine learning methods. Likewise, the comparative experiments have manifested that our process of various biological information is preferable. Conclusions Our proposed deep learning framework effectively identifies essential proteins by integrating multiple biological data, proving a broader selection of subcellular localization information significantly improves the results of prediction and depthwise separable convolution implemented on gene expression profiles enhances the performance.

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A novel essential protein identification method based on PPI networks and gene expression data

Cited by 43 publications

References 49 publications

Construction of dynamic protein interaction network based on gene expression data and quartile one principle

Construction of dynamic protein interaction network based on gene expression data and quartile one principle

Emerging landscape of molecular interaction networks: Opportunities, challenges and prospects

A deep learning framework for identifying essential proteins based on multiple biological information

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