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PEPRF : Identificat...
PEPRF : Identification of Essential Proteins by Integrating Topological Features of PPI Network and Sequence-based Features via Random Forest
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- Wu, Chuanyan (författare)
- Shandong Management University
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- Lin, Bentao (författare)
- Shandong Management University
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- Shi, Kai (författare)
- Shandong Transport Vocational College
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- Zhang, Qingju (författare)
- Shandong University of Science and Technology
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- Gao, Rui (författare)
- Shandong University
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- Yu, Zhiguo (författare)
- Shandong Management University
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- De Marinis, Yang (författare)
- Lund University,Lunds universitet,Genomik, diabetes och endokrinologi,Forskargrupper vid Lunds universitet,Genomics, Diabetes and Endocrinology,Lund University Research Groups
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- Zhang, Yusen (författare)
- Shandong University, Weihai
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- Liu, Zhi Ping (författare)
- Shandong University
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(creator_code:org_t)
- Bentham Science Publishers Ltd. 2021
- 2021
- Engelska 8 s.
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Ingår i: Current Bioinformatics. - : Bentham Science Publishers Ltd.. - 1574-8936. ; 16:9, s. 1161-1168
- Relaterad länk:
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http://dx.doi.org/10...
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https://lup.lub.lu.s...
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https://doi.org/10.2...
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Abstract
Ämnesord
Stäng
- Background: Essential proteins play an important role in the process of life, which can be identified by experimental methods and computational approaches. Experimental approaches to identify essential proteins are of high accuracy but with the limitation of time and resource-consuming. Objective: Herein, we present a computational model (PEPRF) to identify essential proteins based on machine learning. Methods: Different features of proteins were extracted. Topological features of Protein-Protein Interaction (PPI) network-based are extracted. Based on the protein sequence, graph theory-based features, in-formation-based features, composition and physichemical features, etc., were extracted. Finally, 282 features are constructed. In order to select the features that contributed most to the identification, Re-liefF-based feature selection method was adopted to measure the weights of these features. Results: As a result, 212 features were curated to train random forest classifiers. Finally, PEPRF get the AUC of 0.71 and an accuracy of 0.742. Conclusion: Our results show that PEPRF may be applied as an efficient tool to identify essential pro-teins.
Ämnesord
- NATURVETENSKAP -- Biologi -- Bioinformatik och systembiologi (hsv//swe)
- NATURAL SCIENCES -- Biological Sciences -- Bioinformatics and Systems Biology (hsv//eng)
Nyckelord
- Feature extraction
- Graph energy
- PEPRF
- Random forest classi-fier
- ReliefF-based feature selection
- Rma Essential protein prediction
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
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