An Iteration Method for Identifying Yeast Essential Proteins From Weighted PPI Network Based on Topological and Functional Features of Proteins

Accumulating studies have indicated that essential proteins play critical roles in numerous biological processes. With the rapid development of high-throughput technologies, a large number of Protein-Protein Interaction (PPI) data have been found in Saccharomyces cerevisiae, which facilitate the for...
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Gespeichert in:

Autor*in:	Shiyuan Li [verfasserIn] Zhiping Chen [verfasserIn] Xin He [verfasserIn] Zhen Zhang [verfasserIn] Tingrui Pei [verfasserIn] Yihong Tan [verfasserIn] Lei Wang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Characteristic vector orthologous proteins essential proteins weighted protein-protein interaction network iteration method

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 8(2020), Seite 90792-90804
Übergeordnetes Werk:	volume:8 ; year:2020 ; pages:90792-90804

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2020.2993860

Katalog-ID:	DOAJ053357744

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allfields	10.1109/ACCESS.2020.2993860 doi (DE-627)DOAJ053357744 (DE-599)DOAJ17aab379765c4b7b8041ad008e80ae93 DE-627 ger DE-627 rakwb eng TK1-9971 Shiyuan Li verfasserin aut An Iteration Method for Identifying Yeast Essential Proteins From Weighted PPI Network Based on Topological and Functional Features of Proteins 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accumulating studies have indicated that essential proteins play critical roles in numerous biological processes. With the rapid development of high-throughput technologies, a large number of Protein-Protein Interaction (PPI) data have been found in Saccharomyces cerevisiae, which facilitate the formation of PPI networks. Up to now, a series of computational methods for predicting essential proteins from PPI networks have been proposed successively. However, the prediction accuracy of these computational methods is still not quite satisfactory. In this paper, a novel prediction method called CVIM is proposed to infer potential essential proteins. In CVIM, original PPI networks will be first transferred into weighted PPI networks by implementing PCC (Pearson Correlation Coefficient) on protein gene expression data. And then, based on weighted PPI networks and information of orthologous proteins, some critical network topological features and protein functional features will be extracted for each protein in the weighted PPI network. Finally, based on these newly extracted topological and functional features of proteins, an iterative algorithm will be designed to predict essential proteins. In order to evaluate the identification performance of CVIM, we have compared CVIM with 13 kinds of state-of-the-art prediction methods. Experimental results show that CVIM can achieve prediction accuracies of 92%, 80% and 71% out of the top 1%, 5% and 10% candidate proteins separately, which significantly outperform the prediction accuracies achieved by those state-of-the-art prediction methods. We have demonstrated that the prediction accuracy of essential proteins can be effectively improved by integrating the functional and network topological characteristics of proteins, which means that the novel method CVIM may be an excellent addition to the protein researches in the future. Characteristic vector orthologous proteins essential proteins weighted protein-protein interaction network iteration method Electrical engineering. Electronics. Nuclear engineering Zhiping Chen verfasserin aut Xin He verfasserin aut Zhen Zhang verfasserin aut Tingrui Pei verfasserin aut Yihong Tan verfasserin aut Lei Wang verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 90792-90804 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:90792-90804 https://doi.org/10.1109/ACCESS.2020.2993860 kostenfrei https://doaj.org/article/17aab379765c4b7b8041ad008e80ae93 kostenfrei https://ieeexplore.ieee.org/document/9091185/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 90792-90804
spelling	10.1109/ACCESS.2020.2993860 doi (DE-627)DOAJ053357744 (DE-599)DOAJ17aab379765c4b7b8041ad008e80ae93 DE-627 ger DE-627 rakwb eng TK1-9971 Shiyuan Li verfasserin aut An Iteration Method for Identifying Yeast Essential Proteins From Weighted PPI Network Based on Topological and Functional Features of Proteins 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accumulating studies have indicated that essential proteins play critical roles in numerous biological processes. With the rapid development of high-throughput technologies, a large number of Protein-Protein Interaction (PPI) data have been found in Saccharomyces cerevisiae, which facilitate the formation of PPI networks. Up to now, a series of computational methods for predicting essential proteins from PPI networks have been proposed successively. However, the prediction accuracy of these computational methods is still not quite satisfactory. In this paper, a novel prediction method called CVIM is proposed to infer potential essential proteins. In CVIM, original PPI networks will be first transferred into weighted PPI networks by implementing PCC (Pearson Correlation Coefficient) on protein gene expression data. And then, based on weighted PPI networks and information of orthologous proteins, some critical network topological features and protein functional features will be extracted for each protein in the weighted PPI network. Finally, based on these newly extracted topological and functional features of proteins, an iterative algorithm will be designed to predict essential proteins. In order to evaluate the identification performance of CVIM, we have compared CVIM with 13 kinds of state-of-the-art prediction methods. Experimental results show that CVIM can achieve prediction accuracies of 92%, 80% and 71% out of the top 1%, 5% and 10% candidate proteins separately, which significantly outperform the prediction accuracies achieved by those state-of-the-art prediction methods. We have demonstrated that the prediction accuracy of essential proteins can be effectively improved by integrating the functional and network topological characteristics of proteins, which means that the novel method CVIM may be an excellent addition to the protein researches in the future. Characteristic vector orthologous proteins essential proteins weighted protein-protein interaction network iteration method Electrical engineering. Electronics. Nuclear engineering Zhiping Chen verfasserin aut Xin He verfasserin aut Zhen Zhang verfasserin aut Tingrui Pei verfasserin aut Yihong Tan verfasserin aut Lei Wang verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 90792-90804 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:90792-90804 https://doi.org/10.1109/ACCESS.2020.2993860 kostenfrei https://doaj.org/article/17aab379765c4b7b8041ad008e80ae93 kostenfrei https://ieeexplore.ieee.org/document/9091185/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 90792-90804
allfields_unstemmed	10.1109/ACCESS.2020.2993860 doi (DE-627)DOAJ053357744 (DE-599)DOAJ17aab379765c4b7b8041ad008e80ae93 DE-627 ger DE-627 rakwb eng TK1-9971 Shiyuan Li verfasserin aut An Iteration Method for Identifying Yeast Essential Proteins From Weighted PPI Network Based on Topological and Functional Features of Proteins 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accumulating studies have indicated that essential proteins play critical roles in numerous biological processes. With the rapid development of high-throughput technologies, a large number of Protein-Protein Interaction (PPI) data have been found in Saccharomyces cerevisiae, which facilitate the formation of PPI networks. Up to now, a series of computational methods for predicting essential proteins from PPI networks have been proposed successively. However, the prediction accuracy of these computational methods is still not quite satisfactory. In this paper, a novel prediction method called CVIM is proposed to infer potential essential proteins. In CVIM, original PPI networks will be first transferred into weighted PPI networks by implementing PCC (Pearson Correlation Coefficient) on protein gene expression data. And then, based on weighted PPI networks and information of orthologous proteins, some critical network topological features and protein functional features will be extracted for each protein in the weighted PPI network. Finally, based on these newly extracted topological and functional features of proteins, an iterative algorithm will be designed to predict essential proteins. In order to evaluate the identification performance of CVIM, we have compared CVIM with 13 kinds of state-of-the-art prediction methods. Experimental results show that CVIM can achieve prediction accuracies of 92%, 80% and 71% out of the top 1%, 5% and 10% candidate proteins separately, which significantly outperform the prediction accuracies achieved by those state-of-the-art prediction methods. We have demonstrated that the prediction accuracy of essential proteins can be effectively improved by integrating the functional and network topological characteristics of proteins, which means that the novel method CVIM may be an excellent addition to the protein researches in the future. Characteristic vector orthologous proteins essential proteins weighted protein-protein interaction network iteration method Electrical engineering. Electronics. Nuclear engineering Zhiping Chen verfasserin aut Xin He verfasserin aut Zhen Zhang verfasserin aut Tingrui Pei verfasserin aut Yihong Tan verfasserin aut Lei Wang verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 90792-90804 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:90792-90804 https://doi.org/10.1109/ACCESS.2020.2993860 kostenfrei https://doaj.org/article/17aab379765c4b7b8041ad008e80ae93 kostenfrei https://ieeexplore.ieee.org/document/9091185/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 90792-90804
allfieldsGer	10.1109/ACCESS.2020.2993860 doi (DE-627)DOAJ053357744 (DE-599)DOAJ17aab379765c4b7b8041ad008e80ae93 DE-627 ger DE-627 rakwb eng TK1-9971 Shiyuan Li verfasserin aut An Iteration Method for Identifying Yeast Essential Proteins From Weighted PPI Network Based on Topological and Functional Features of Proteins 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accumulating studies have indicated that essential proteins play critical roles in numerous biological processes. With the rapid development of high-throughput technologies, a large number of Protein-Protein Interaction (PPI) data have been found in Saccharomyces cerevisiae, which facilitate the formation of PPI networks. Up to now, a series of computational methods for predicting essential proteins from PPI networks have been proposed successively. However, the prediction accuracy of these computational methods is still not quite satisfactory. In this paper, a novel prediction method called CVIM is proposed to infer potential essential proteins. In CVIM, original PPI networks will be first transferred into weighted PPI networks by implementing PCC (Pearson Correlation Coefficient) on protein gene expression data. And then, based on weighted PPI networks and information of orthologous proteins, some critical network topological features and protein functional features will be extracted for each protein in the weighted PPI network. Finally, based on these newly extracted topological and functional features of proteins, an iterative algorithm will be designed to predict essential proteins. In order to evaluate the identification performance of CVIM, we have compared CVIM with 13 kinds of state-of-the-art prediction methods. Experimental results show that CVIM can achieve prediction accuracies of 92%, 80% and 71% out of the top 1%, 5% and 10% candidate proteins separately, which significantly outperform the prediction accuracies achieved by those state-of-the-art prediction methods. We have demonstrated that the prediction accuracy of essential proteins can be effectively improved by integrating the functional and network topological characteristics of proteins, which means that the novel method CVIM may be an excellent addition to the protein researches in the future. Characteristic vector orthologous proteins essential proteins weighted protein-protein interaction network iteration method Electrical engineering. Electronics. Nuclear engineering Zhiping Chen verfasserin aut Xin He verfasserin aut Zhen Zhang verfasserin aut Tingrui Pei verfasserin aut Yihong Tan verfasserin aut Lei Wang verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 90792-90804 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:90792-90804 https://doi.org/10.1109/ACCESS.2020.2993860 kostenfrei https://doaj.org/article/17aab379765c4b7b8041ad008e80ae93 kostenfrei https://ieeexplore.ieee.org/document/9091185/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 90792-90804
allfieldsSound	10.1109/ACCESS.2020.2993860 doi (DE-627)DOAJ053357744 (DE-599)DOAJ17aab379765c4b7b8041ad008e80ae93 DE-627 ger DE-627 rakwb eng TK1-9971 Shiyuan Li verfasserin aut An Iteration Method for Identifying Yeast Essential Proteins From Weighted PPI Network Based on Topological and Functional Features of Proteins 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accumulating studies have indicated that essential proteins play critical roles in numerous biological processes. With the rapid development of high-throughput technologies, a large number of Protein-Protein Interaction (PPI) data have been found in Saccharomyces cerevisiae, which facilitate the formation of PPI networks. Up to now, a series of computational methods for predicting essential proteins from PPI networks have been proposed successively. However, the prediction accuracy of these computational methods is still not quite satisfactory. In this paper, a novel prediction method called CVIM is proposed to infer potential essential proteins. In CVIM, original PPI networks will be first transferred into weighted PPI networks by implementing PCC (Pearson Correlation Coefficient) on protein gene expression data. And then, based on weighted PPI networks and information of orthologous proteins, some critical network topological features and protein functional features will be extracted for each protein in the weighted PPI network. Finally, based on these newly extracted topological and functional features of proteins, an iterative algorithm will be designed to predict essential proteins. In order to evaluate the identification performance of CVIM, we have compared CVIM with 13 kinds of state-of-the-art prediction methods. Experimental results show that CVIM can achieve prediction accuracies of 92%, 80% and 71% out of the top 1%, 5% and 10% candidate proteins separately, which significantly outperform the prediction accuracies achieved by those state-of-the-art prediction methods. We have demonstrated that the prediction accuracy of essential proteins can be effectively improved by integrating the functional and network topological characteristics of proteins, which means that the novel method CVIM may be an excellent addition to the protein researches in the future. Characteristic vector orthologous proteins essential proteins weighted protein-protein interaction network iteration method Electrical engineering. Electronics. Nuclear engineering Zhiping Chen verfasserin aut Xin He verfasserin aut Zhen Zhang verfasserin aut Tingrui Pei verfasserin aut Yihong Tan verfasserin aut Lei Wang verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 90792-90804 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:90792-90804 https://doi.org/10.1109/ACCESS.2020.2993860 kostenfrei https://doaj.org/article/17aab379765c4b7b8041ad008e80ae93 kostenfrei https://ieeexplore.ieee.org/document/9091185/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 90792-90804
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callnumber-first	T - Technology
author	Shiyuan Li
spellingShingle	Shiyuan Li misc TK1-9971 misc Characteristic vector misc orthologous proteins misc essential proteins misc weighted protein-protein interaction network misc iteration method misc Electrical engineering. Electronics. Nuclear engineering An Iteration Method for Identifying Yeast Essential Proteins From Weighted PPI Network Based on Topological and Functional Features of Proteins
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topic_title	TK1-9971 An Iteration Method for Identifying Yeast Essential Proteins From Weighted PPI Network Based on Topological and Functional Features of Proteins Characteristic vector orthologous proteins essential proteins weighted protein-protein interaction network iteration method
topic	misc TK1-9971 misc Characteristic vector misc orthologous proteins misc essential proteins misc weighted protein-protein interaction network misc iteration method misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Characteristic vector misc orthologous proteins misc essential proteins misc weighted protein-protein interaction network misc iteration method misc Electrical engineering. Electronics. Nuclear engineering
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title	An Iteration Method for Identifying Yeast Essential Proteins From Weighted PPI Network Based on Topological and Functional Features of Proteins
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title_full	An Iteration Method for Identifying Yeast Essential Proteins From Weighted PPI Network Based on Topological and Functional Features of Proteins
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title_sort	iteration method for identifying yeast essential proteins from weighted ppi network based on topological and functional features of proteins
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title_auth	An Iteration Method for Identifying Yeast Essential Proteins From Weighted PPI Network Based on Topological and Functional Features of Proteins
abstract	Accumulating studies have indicated that essential proteins play critical roles in numerous biological processes. With the rapid development of high-throughput technologies, a large number of Protein-Protein Interaction (PPI) data have been found in Saccharomyces cerevisiae, which facilitate the formation of PPI networks. Up to now, a series of computational methods for predicting essential proteins from PPI networks have been proposed successively. However, the prediction accuracy of these computational methods is still not quite satisfactory. In this paper, a novel prediction method called CVIM is proposed to infer potential essential proteins. In CVIM, original PPI networks will be first transferred into weighted PPI networks by implementing PCC (Pearson Correlation Coefficient) on protein gene expression data. And then, based on weighted PPI networks and information of orthologous proteins, some critical network topological features and protein functional features will be extracted for each protein in the weighted PPI network. Finally, based on these newly extracted topological and functional features of proteins, an iterative algorithm will be designed to predict essential proteins. In order to evaluate the identification performance of CVIM, we have compared CVIM with 13 kinds of state-of-the-art prediction methods. Experimental results show that CVIM can achieve prediction accuracies of 92%, 80% and 71% out of the top 1%, 5% and 10% candidate proteins separately, which significantly outperform the prediction accuracies achieved by those state-of-the-art prediction methods. We have demonstrated that the prediction accuracy of essential proteins can be effectively improved by integrating the functional and network topological characteristics of proteins, which means that the novel method CVIM may be an excellent addition to the protein researches in the future.
abstractGer	Accumulating studies have indicated that essential proteins play critical roles in numerous biological processes. With the rapid development of high-throughput technologies, a large number of Protein-Protein Interaction (PPI) data have been found in Saccharomyces cerevisiae, which facilitate the formation of PPI networks. Up to now, a series of computational methods for predicting essential proteins from PPI networks have been proposed successively. However, the prediction accuracy of these computational methods is still not quite satisfactory. In this paper, a novel prediction method called CVIM is proposed to infer potential essential proteins. In CVIM, original PPI networks will be first transferred into weighted PPI networks by implementing PCC (Pearson Correlation Coefficient) on protein gene expression data. And then, based on weighted PPI networks and information of orthologous proteins, some critical network topological features and protein functional features will be extracted for each protein in the weighted PPI network. Finally, based on these newly extracted topological and functional features of proteins, an iterative algorithm will be designed to predict essential proteins. In order to evaluate the identification performance of CVIM, we have compared CVIM with 13 kinds of state-of-the-art prediction methods. Experimental results show that CVIM can achieve prediction accuracies of 92%, 80% and 71% out of the top 1%, 5% and 10% candidate proteins separately, which significantly outperform the prediction accuracies achieved by those state-of-the-art prediction methods. We have demonstrated that the prediction accuracy of essential proteins can be effectively improved by integrating the functional and network topological characteristics of proteins, which means that the novel method CVIM may be an excellent addition to the protein researches in the future.
abstract_unstemmed	Accumulating studies have indicated that essential proteins play critical roles in numerous biological processes. With the rapid development of high-throughput technologies, a large number of Protein-Protein Interaction (PPI) data have been found in Saccharomyces cerevisiae, which facilitate the formation of PPI networks. Up to now, a series of computational methods for predicting essential proteins from PPI networks have been proposed successively. However, the prediction accuracy of these computational methods is still not quite satisfactory. In this paper, a novel prediction method called CVIM is proposed to infer potential essential proteins. In CVIM, original PPI networks will be first transferred into weighted PPI networks by implementing PCC (Pearson Correlation Coefficient) on protein gene expression data. And then, based on weighted PPI networks and information of orthologous proteins, some critical network topological features and protein functional features will be extracted for each protein in the weighted PPI network. Finally, based on these newly extracted topological and functional features of proteins, an iterative algorithm will be designed to predict essential proteins. In order to evaluate the identification performance of CVIM, we have compared CVIM with 13 kinds of state-of-the-art prediction methods. Experimental results show that CVIM can achieve prediction accuracies of 92%, 80% and 71% out of the top 1%, 5% and 10% candidate proteins separately, which significantly outperform the prediction accuracies achieved by those state-of-the-art prediction methods. We have demonstrated that the prediction accuracy of essential proteins can be effectively improved by integrating the functional and network topological characteristics of proteins, which means that the novel method CVIM may be an excellent addition to the protein researches in the future.
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title_short	An Iteration Method for Identifying Yeast Essential Proteins From Weighted PPI Network Based on Topological and Functional Features of Proteins
url	https://doi.org/10.1109/ACCESS.2020.2993860 https://doaj.org/article/17aab379765c4b7b8041ad008e80ae93 https://ieeexplore.ieee.org/document/9091185/ https://doaj.org/toc/2169-3536
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up_date	2024-07-03T17:13:39.602Z
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An Iteration Method for Identifying Yeast Essential Proteins From Weighted PPI Network Based on Topological and Functional Features of Proteins

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