A Novel Network-Based Computational Model for Prediction of Essential Proteins
Identification of essential proteins is important for understanding cell survival and development, because even if only one of these proteins is missing, organisms cannot survive or develop. Since traditional methods for identifying essential proteins based on biological experiments are costly and i...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9151981/ |
| _version_ | 1818323786387161088 |
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| author | Xianyou Zhu Yang Liu Tingrui Pei Zhiping Chen Xueyong Li Wang Lei |
| author_facet | Xianyou Zhu Yang Liu Tingrui Pei Zhiping Chen Xueyong Li Wang Lei |
| author_sort | Xianyou Zhu |
| collection | DOAJ |
| description | Identification of essential proteins is important for understanding cell survival and development, because even if only one of these proteins is missing, organisms cannot survive or develop. Since traditional methods for identifying essential proteins based on biological experiments are costly and inefficient, more and more computational models are proposed for predicting essential proteins in recent years. In this paper, a novel computational model called BSPM is proposed, in which, an original PPI network will be built based on known protein-protein associations first, and then topology information of the original PPI network will be adopted to measure the similarities between proteins based on the SimRank algorithm. Thereafter, a weighted PPI network can be obtained based on the similarities between proteins and the original PPI network. Finally, based on the weighted PPI network, the PageRank algorithm will be used to infer potential essential proteins. Moreover, in order to evaluate the performance of BSPM, we have compared the performance of BSPM with 14 classical prediction models in the field based on two different databases, and experimental results show that BSPM can achieve prediction accuracies of 92%, 81% and 76% out of the top 100, 200 and 300 candidate proteins separately, which not only are significantly better than those 14 competitive classical prediction models, but also means that BSPM can be used as an effective model for identifying essential proteins in the future. |
| first_indexed | 2024-12-13T11:18:13Z |
| format | Article |
| id | doaj.art-14722bf27ee944ae9c8550bf46d7a13f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T11:18:13Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-14722bf27ee944ae9c8550bf46d7a13f2022-12-21T23:48:34ZengIEEEIEEE Access2169-35362020-01-01813814113814810.1109/ACCESS.2020.30126829151981A Novel Network-Based Computational Model for Prediction of Essential ProteinsXianyou Zhu0Yang Liu1Tingrui Pei2https://orcid.org/0000-0002-1205-5899Zhiping Chen3https://orcid.org/0000-0003-4759-3774Xueyong Li4Wang Lei5https://orcid.org/0000-0002-5065-8447Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang Normal University, Hengyang, ChinaCollege of Computer Engineering and Applied Mathematics, Changsha University, Changsha, ChinaKey Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, ChinaCollege of Computer Engineering and Applied Mathematics, Changsha University, Changsha, ChinaCollege of Computer Engineering and Applied Mathematics, Changsha University, Changsha, ChinaCollege of Computer Engineering and Applied Mathematics, Changsha University, Changsha, ChinaIdentification of essential proteins is important for understanding cell survival and development, because even if only one of these proteins is missing, organisms cannot survive or develop. Since traditional methods for identifying essential proteins based on biological experiments are costly and inefficient, more and more computational models are proposed for predicting essential proteins in recent years. In this paper, a novel computational model called BSPM is proposed, in which, an original PPI network will be built based on known protein-protein associations first, and then topology information of the original PPI network will be adopted to measure the similarities between proteins based on the SimRank algorithm. Thereafter, a weighted PPI network can be obtained based on the similarities between proteins and the original PPI network. Finally, based on the weighted PPI network, the PageRank algorithm will be used to infer potential essential proteins. Moreover, in order to evaluate the performance of BSPM, we have compared the performance of BSPM with 14 classical prediction models in the field based on two different databases, and experimental results show that BSPM can achieve prediction accuracies of 92%, 81% and 76% out of the top 100, 200 and 300 candidate proteins separately, which not only are significantly better than those 14 competitive classical prediction models, but also means that BSPM can be used as an effective model for identifying essential proteins in the future.https://ieeexplore.ieee.org/document/9151981/Essential proteinPPISimRank algorithmPageRank algorithm |
| spellingShingle | Xianyou Zhu Yang Liu Tingrui Pei Zhiping Chen Xueyong Li Wang Lei A Novel Network-Based Computational Model for Prediction of Essential Proteins IEEE Access Essential protein PPI SimRank algorithm PageRank algorithm |
| title | A Novel Network-Based Computational Model for Prediction of Essential Proteins |
| title_full | A Novel Network-Based Computational Model for Prediction of Essential Proteins |
| title_fullStr | A Novel Network-Based Computational Model for Prediction of Essential Proteins |
| title_full_unstemmed | A Novel Network-Based Computational Model for Prediction of Essential Proteins |
| title_short | A Novel Network-Based Computational Model for Prediction of Essential Proteins |
| title_sort | novel network based computational model for prediction of essential proteins |
| topic | Essential protein PPI SimRank algorithm PageRank algorithm |
| url | https://ieeexplore.ieee.org/document/9151981/ |
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