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Identifying essential proteins from protein–protein interaction networks based on influence maximization

BACKGROUND: Essential proteins are indispensable to the development and survival of cells. The identification of essential proteins not only is helpful for the understanding of the minimal requirements for cell survival, but also has practical significance in disease diagnosis, drug design and medic...

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Autores principales: Xu, Weixia, Dong, Yunfeng, Guan, Jihong, Zhou, Shuigeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9380286/
https://www.ncbi.nlm.nih.gov/pubmed/35974329
http://dx.doi.org/10.1186/s12859-022-04874-w
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author Xu, Weixia
Dong, Yunfeng
Guan, Jihong
Zhou, Shuigeng
author_facet Xu, Weixia
Dong, Yunfeng
Guan, Jihong
Zhou, Shuigeng
author_sort Xu, Weixia
collection PubMed
description BACKGROUND: Essential proteins are indispensable to the development and survival of cells. The identification of essential proteins not only is helpful for the understanding of the minimal requirements for cell survival, but also has practical significance in disease diagnosis, drug design and medical treatment. With the rapidly amassing of protein–protein interaction (PPI) data, computationally identifying essential proteins from protein–protein interaction networks (PINs) becomes more and more popular. Up to now, a number of various approaches for essential protein identification based on PINs have been developed. RESULTS: In this paper, we propose a new and effective approach called iMEPP to identify essential proteins from PINs by fusing multiple types of biological data and applying the influence maximization mechanism to the PINs. Concretely, we first integrate PPI data, gene expression data and Gene Ontology to construct weighted PINs, to alleviate the impact of high false-positives in the raw PPI data. Then, we define the influence scores of nodes in PINs with both orthological data and PIN topological information. Finally, we develop an influence discount algorithm to identify essential proteins based on the influence maximization mechanism. CONCLUSIONS: We applied our method to identifying essential proteins from saccharomyces cerevisiae PIN. Experiments show that our iMEPP method outperforms the existing methods, which validates its effectiveness and advantage.
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spelling pubmed-93802862022-08-17 Identifying essential proteins from protein–protein interaction networks based on influence maximization Xu, Weixia Dong, Yunfeng Guan, Jihong Zhou, Shuigeng BMC Bioinformatics Research BACKGROUND: Essential proteins are indispensable to the development and survival of cells. The identification of essential proteins not only is helpful for the understanding of the minimal requirements for cell survival, but also has practical significance in disease diagnosis, drug design and medical treatment. With the rapidly amassing of protein–protein interaction (PPI) data, computationally identifying essential proteins from protein–protein interaction networks (PINs) becomes more and more popular. Up to now, a number of various approaches for essential protein identification based on PINs have been developed. RESULTS: In this paper, we propose a new and effective approach called iMEPP to identify essential proteins from PINs by fusing multiple types of biological data and applying the influence maximization mechanism to the PINs. Concretely, we first integrate PPI data, gene expression data and Gene Ontology to construct weighted PINs, to alleviate the impact of high false-positives in the raw PPI data. Then, we define the influence scores of nodes in PINs with both orthological data and PIN topological information. Finally, we develop an influence discount algorithm to identify essential proteins based on the influence maximization mechanism. CONCLUSIONS: We applied our method to identifying essential proteins from saccharomyces cerevisiae PIN. Experiments show that our iMEPP method outperforms the existing methods, which validates its effectiveness and advantage. BioMed Central 2022-08-16 /pmc/articles/PMC9380286/ /pubmed/35974329 http://dx.doi.org/10.1186/s12859-022-04874-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Xu, Weixia
Dong, Yunfeng
Guan, Jihong
Zhou, Shuigeng
Identifying essential proteins from protein–protein interaction networks based on influence maximization
title Identifying essential proteins from protein–protein interaction networks based on influence maximization
title_full Identifying essential proteins from protein–protein interaction networks based on influence maximization
title_fullStr Identifying essential proteins from protein–protein interaction networks based on influence maximization
title_full_unstemmed Identifying essential proteins from protein–protein interaction networks based on influence maximization
title_short Identifying essential proteins from protein–protein interaction networks based on influence maximization
title_sort identifying essential proteins from protein–protein interaction networks based on influence maximization
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9380286/
https://www.ncbi.nlm.nih.gov/pubmed/35974329
http://dx.doi.org/10.1186/s12859-022-04874-w
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