scholarly journals An iteration model for identifying essential proteins by combining comprehensive PPI network with biological information

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Shiyuan Li ◽  
Zhen Zhang ◽  
Xueyong Li ◽  
Yihong Tan ◽  
Lei Wang ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Computational Models ◽  
Density Measurement ◽  
Network Connectivity ◽  
Interaction Network ◽  
Biological Information ◽  
Detection Methods ◽  
Essential Proteins ◽  
Interactive Network ◽  
Survival And Development

Abstract Background Essential proteins have great impacts on cell survival and development, and played important roles in disease analysis and new drug design. However, since it is inefficient and costly to identify essential proteins by using biological experiments, then there is an urgent need for automated and accurate detection methods. In recent years, the recognition of essential proteins in protein interaction networks (PPI) has become a research hotspot, and many computational models for predicting essential proteins have been proposed successively. Results In order to achieve higher prediction performance, in this paper, a new prediction model called TGSO is proposed. In TGSO, a protein aggregation degree network is constructed first by adopting the node density measurement method for complex networks. And simultaneously, a protein co-expression interactive network is constructed by combining the gene expression information with the network connectivity, and a protein co-localization interaction network is constructed based on the subcellular localization data. And then, through integrating these three kinds of newly constructed networks, a comprehensive protein–protein interaction network will be obtained. Finally, based on the homology information, scores can be calculated out iteratively for different proteins, which can be utilized to estimate the importance of proteins effectively. Moreover, in order to evaluate the identification performance of TGSO, we have compared TGSO with 13 different latest competitive methods based on three kinds of yeast databases. And experimental results show that TGSO can achieve identification accuracies of 94%, 82% and 72% out of the top 1%, 5% and 10% candidate proteins respectively, which are to some degree superior to these state-of-the-art competitive models. Conclusions We constructed a comprehensive interactive network based on multi-source data to reduce the noise and errors in the initial PPI, and combined with iterative methods to improve the accuracy of necessary protein prediction, and means that TGSO may be conducive to the future development of essential protein recognition as well.

scholarly journals An Iteration Model for Identifying Essential Proteins by Combining Comprehensive PPI Network With Biological Information

2020 ◽  
Author(s):  
Shiyuan Li ◽  
Zhen Zhang ◽  
Xueyong Li ◽  
Yihong Tan ◽  
Lei Wang ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Computational Models ◽  
Density Measurement ◽  
Network Connectivity ◽  
Interaction Network ◽  
Biological Information ◽  
Detection Methods ◽  
Essential Proteins ◽  
Interactive Network ◽  
Survival And Development

Abstract Background: Essential proteins have great impacts on cell survival and development, and played important roles in disease analysis and new drug design. However, since it is inefficient and costly to identify essential proteins by using biological experiments, then there is an urgent need for automated and accurate detection methods. In recent years, the recognition of essential proteins in protein interaction networks (PPI) has become a research hotspot, and many computational models for predicting essential proteins have been proposed successively.Results: In order to achieve higher prediction performance, in this paper, a new prediction model called TGSO is proposed. In TGSO, a protein aggregation degree network is constructed first by adopting the node density measurement method for complex networks. And simultaneously, a protein co-expression interactive network is constructed by combining the gene expression information with the network connectivity, and a protein co-localization interaction network is constructed based on the subcellular localization data. And then, through integrating these three kinds of newly constructed networks, a comprehensive protein-protein interaction network will be obtained. Finally, based on the homology information, scores can be calculated out iteratively for different proteins, which can be utilized to estimate the importance of proteins effectively. Moreover, in order to evaluate the identification performance of TGSO, we have compared TGSO with 13 different latest competitive methods based on three kinds of yeast databases. And experimental results show that TGSO can achieve identification accuracies of 94\%, 82\% and 72\% out of the top 1\%, 5\% and 10\% candidate proteins respectively, which are to some degree superior to these state-of-the-art competitive models. Conclusion: We constructed a comprehensive interactive network based on multi-source data to reduce the noise and errors in the initial PPI, and combined with iterative methods to improve the accuracy of necessary protein prediction, and means that TGSO may be conducive to the future development of essential protein recognition as well.

Identification of Important Proteins in Protein Interaction Network Based on SVM

2014 ◽  
Vol 644-650 ◽  
pp. 5202-5206
Author(s):  
Yan Li Zha ◽  
Wan Cheng Luo
Keyword(s):  
Protein Interaction ◽  
Interaction Network ◽  
Biological Information ◽  
Yeast Protein ◽  
Essential Proteins ◽  
Svm Model ◽  
Network Topologies ◽  
Living Organisms ◽  
Yeast Protein Interaction ◽  
Relevant Experiment

Importance of proteins are different to perform functions of cells in living organisms according to the relevant experiment results, and more essential proteins is the most important kind of proteins. There are recently many computational approaches proposed to predict essential proteins in network level through network topologies combined with biological information of proteins. However it is still hard to identify them because of limitations of topological centralities and bioinformatic sources. And more it is the challenge is to perform better with less resources. Therefore in this paper, we first examine the correlation between common topological centralities and essential proteins and choose a few particular centralities, and then to build a SVM model, names as TC-SVM, for predicting the essential proteins. The new method has been applied to a yeast protein interaction networks, which are obtained from the BioGRID database. The ten folds experimental results show that the performance of predicting essential proteins by TC-SVM is excellent.

scholarly journals A Novel Method for Identifying Essential Proteins Based on Non-negative Matrix Tri-Factorization

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhihong Zhang ◽  
Meiping Jiang ◽  
Dongjie Wu ◽  
Wang Zhang ◽  
Wei Yan ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Negative Impact ◽  
False Negative ◽  
Interaction Network ◽  
Biological Information ◽  
Ppi Network ◽  
Essential Proteins ◽  
Protein Protein Interaction ◽  
Ppi Networks

Identification of essential proteins is very important for understanding the basic requirements to sustain a living organism. In recent years, there has been an increasing interest in using computational methods to predict essential proteins based on protein–protein interaction (PPI) networks or fusing multiple biological information. However, it has been observed that existing PPI data have false-negative and false-positive data. The fusion of multiple biological information can reduce the influence of false data in PPI, but inevitably more noise data will be produced at the same time. In this article, we proposed a novel non-negative matrix tri-factorization (NMTF)-based model (NTMEP) to predict essential proteins. Firstly, a weighted PPI network is established only using the topology features of the network, so as to avoid more noise. To reduce the influence of false data (existing in PPI network) on performance of identify essential proteins, the NMTF technique, as a widely used recommendation algorithm, is performed to reconstruct a most optimized PPI network with more potential protein–protein interactions. Then, we use the PageRank algorithm to compute the final ranking score of each protein, in which subcellular localization and homologous information of proteins were used to calculate the initial scores. In addition, extensive experiments are performed on the publicly available datasets and the results indicate that our NTMEP model has better performance in predicting essential proteins against the start-of-the-art method. In this investigation, we demonstrated that the introduction of non-negative matrix tri-factorization technology can effectively improve the condition of the protein–protein interaction network, so as to reduce the negative impact of noise on the prediction. At the same time, this finding provides a more novel angle of view for other applications based on protein–protein interaction networks.

scholarly journals A Non-negative Matrix Factorization Based Method for Identifying Essential Proteins

2021 ◽  
Author(s):  
Zhihong Zhang ◽  
Sai Hu ◽  
Wei Yan ◽  
Bihai Zhao ◽  
Lei Wang
Keyword(s):  
Protein Interaction ◽  
Matrix Factorization ◽  
Biological Data ◽  
Protein Domain ◽  
Biological Information ◽  
Ppi Network ◽  
Essential Proteins ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Non Negative Matrix Factorization

Abstract BackgroundIdentification of essential proteins is very important for understanding the basic requirements to sustain a living organism. In recent years, various different computational methods have been proposed to identify essential proteins based on protein-protein interaction (PPI) networks. However, there has been reliable evidence that a huge amount of false negatives and false positives exist in PPI data. Therefore, it is necessary to reduce the influence of false data on accuracy of essential proteins prediction by integrating multi-source biological information with PPI networks.ResultsIn this paper, we proposed a non-negative matrix factorization and multiple biological information based model (NDM) for identifying essential proteins. The first stage in this progress was to construct a weighted PPI network by combing the information of protein domain, protein complex and the topology characteristic of the original PPI network. Then, the non-negative matrix factorization technique was used to reconstruct an optimized PPI network with whole enough weight of edges. In the final stage, the ranking score of each protein was computed by the PageRank algorithm in which the initial scores were calculated with homologous and subcellular localization information. In order to verify the effectiveness of the NDM method, we compared the NDM with other state-of-the-art essential proteins prediction methods. The comparison of the results obtained from different methods indicated that our NDM model has better performance in predicting essential proteins.ConclusionEmploying the non-negative matrix factorization and integrating multi-source biological data can effectively improve quality of the PPI network, which resulted in the led to optimization of the performance essential proteins identification. This will also provide a new perspective for other prediction based on protein-protein interaction networks.

scholarly journals Comparing protein-protein interaction networks of SARS-CoV-2 and (H1N1) influenza using topological features

2021 ◽  
Author(s):  
Hakimeh Khojasteh ◽  
Alireza Khanteymoori ◽  
Mohammad Hossein Olyaee
Keyword(s):  
Dimensionality Reduction ◽  
Power Law ◽  
Protein Interaction ◽  
Interaction Network ◽  
H1n1 Influenza ◽  
Centrality Measures ◽  
Essential Proteins ◽  
Protein Protein Interaction ◽  
Topological Features ◽  
High Level

Background: SARS-CoV-2 pandemic first emerged in late 2019 in China. It has since infected more than 183 million individuals and caused about 4 million deaths globally. A protein-protein interaction network (PPIN) and its analysis can provide insight into the behavior of cells and lead to advance the procedure of drug discovery. The identification of essential proteins is crucial to understand for cellular survival. There are many centrality measures to detect influential nodes in complex networks. Since SARS-CoV-2 and (H1N1) influenza PPINs pose 553 common proteins. Analyzing influential proteins and comparing these networks together can be an effective step helping biologists in drug design. Results: We used 21 centrality measures on SARS-CoV-2 and (H1N1) influenza PPINs to identify essential proteins. PCA-based dimensionality reduction was applied on normalized centrality values. Some measures demonstrated a high level of contribution in comparison to others in both PPINs, like Barycenter, Decay, Diffusion degree, Closeness (Freeman), Closeness (Latora), Lin, Radiality, and Residual. Using validation measures, the appropriate clustering method was chosen for centrality measures. We also investigated some graph theory-based properties like the power law, exponential distribution, and robustness. Conclusions: Through analysis and comparison, both networks exhibited remarkable experimental results. The network diameters were equal and in terms of heterogeneity, SARS-CoV-2 PPIN tends to be more heterogeneous. Both networks under study display a typical power-law degree distribution. Dimensionality reduction and unsupervised learning methods were so effective to reveal appropriate centrality measures.

scholarly journals Network analysis reveals essential proteins that regulate sodium-iodide symporter expression in anaplastic thyroid carcinoma

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Hassan Rakhsh-Khorshid ◽  
Hilda Samimi ◽  
Shukoofeh Torabi ◽  
Sayed Mahmoud Sajjadi-Jazi ◽  
Hamed Samadi ◽  
...  
Keyword(s):  
Thyroid Carcinoma ◽  
Information Flow ◽  
Protein Interaction ◽  
Biological Networks ◽  
Protein Interaction Network ◽  
Sodium Iodide ◽  
Interaction Network ◽  
Anaplastic Thyroid Carcinoma ◽  
Sodium Iodide Symporter ◽  
Essential Proteins

AbstractAnaplastic thyroid carcinoma (ATC) is the most rare and lethal form of thyroid cancer and requires effective treatment. Efforts have been made to restore sodium-iodide symporter (NIS) expression in ATC cells where it has been downregulated, yet without complete success. Systems biology approaches have been used to simplify complex biological networks. Here, we attempt to find more suitable targets in order to restore NIS expression in ATC cells. We have built a simplified protein interaction network including transcription factors and proteins involved in MAPK, TGFβ/SMAD, PI3K/AKT, and TSHR signaling pathways which regulate NIS expression, alongside proteins interacting with them. The network was analyzed, and proteins were ranked based on several centrality indices. Our results suggest that the protein interaction network of NIS expression regulation is modular, and distance-based and information-flow-based centrality indices may be better predictors of important proteins in such networks. We propose that the high-ranked proteins found in our analysis are expected to be more promising targets in attempts to restore NIS expression in ATC cells.

scholarly journals PINOT: An Intuitive Resource for Integrating Protein-Protein Interactions

2019 ◽  
Author(s):  
JE Tomkins ◽  
R Ferrari ◽  
N Vavouraki ◽  
J Hardy ◽  
RC Lovering ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Data Entry ◽  
Interaction Network ◽  
Ease Of Use ◽  
Detection Methods ◽  
C Elegans ◽  
Protein Protein Interaction ◽  
Web Resource ◽  
Ppi Networks

AbstractThe past decade has seen the rise of omics data, for the understanding of biological systems in health and disease. This wealth of data includes protein-protein interaction (PPI) derived from both low and high-throughput assays, which is curated into multiple databases that capture the extent of available information from the peer-reviewed literature. Although these curation efforts are extremely useful, reliably downloading and integrating PPI data from the variety of available repositories is challenging and time consuming.We here present a novel user-friendly web-resource called PINOT (Protein Interaction Network Online Tool; available at http://www.reading.ac.uk/bioinf/PINOT/PINOT_form.html) to optimise the collection and processing of PPI data from the IMEx consortium associated repositories (members and observers) and from WormBase for constructing, respectively, human and C. elegans PPI networks.Users submit a query containing a list of proteins of interest for which PINOT will mine PPIs. PPI data is downloaded, merged, quality checked, and confidence scored based on the number of distinct methods and publications in which each interaction has been reported. Examples of PINOT applications are provided to highlight the performance, the ease of use and the potential applications of this tool.PINOT is a tool that allows users to survey the literature, extracting PPI data for a list of proteins of interest. The comparison with analogous tools showed that PINOT was able to extract similar numbers of PPIs while incorporating a set of innovative features. PINOT processes both small and large queries, it downloads PPIs live through PSICQUIC and it applies quality control filters on the downloaded PPI annotations (i.e. removing the need of manual inspection by the user). PINOT provides the user with information on detection methods and publication history for each of the downloaded interaction data entry and provides results in a table format that can be easily further customised and/or directly uploaded in a network visualization software.

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