Predicting Essential Genes and Proteins Based on Machine Learning and Network Topological Features: A Comprehensive Review

dc.contributor.authorZhang, Xue
dc.contributor.authorAcencio, Marcio Luis [UNESP]
dc.contributor.authorLemke, Ney [UNESP]
dc.contributor.institutionXiangnan Univ
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionNorwegian Univ Sci & Technol
dc.date.accessioned2018-11-26T16:27:50Z
dc.date.available2018-11-26T16:27:50Z
dc.date.issued2016-03-08
dc.description.abstractEssential proteins/genes are indispensable to the survival or reproduction of an organism, and the deletion of such essential proteins will result in lethality or infertility. The identification of essential genes is very important not only for understanding the minimal requirements for survival of an organism, but also for finding human disease genes and new drug targets. Experimental methods for identifying essential genes are costly, time-consuming, and laborious. With the accumulation of sequenced genomes data and high-throughput experimental data, many computational methods for identifying essential proteins are proposed, which are useful complements to experimental methods. In this review, we show the state-of-the-art methods for identifying essential genes and proteins based on machine learning and network topological features, point out the progress and limitations of current methods, and discuss the challenges and directions for further research.en
dc.description.affiliationXiangnan Univ, Dept Comp Sci, Chenzhou, Hunan, Peoples R China
dc.description.affiliationSao Paulo State Univ, Inst Biosci Botucatu, Dept Phys & Biophys, Botucatu, SP, Brazil
dc.description.affiliationNorwegian Univ Sci & Technol, Dept Canc Res & Mol Med, Fac Med, N-7034 Trondheim, Norway
dc.description.affiliationUnespSao Paulo State Univ, Inst Biosci Botucatu, Dept Phys & Biophys, Botucatu, SP, Brazil
dc.description.sponsorshipNational Natural Science Foundation of China
dc.description.sponsorshipGuizhou Provincial Science and Technology Fund
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.description.sponsorshipIdNational Natural Science Foundation of China: 61402423
dc.description.sponsorshipIdNational Natural Science Foundation of China: 61502343
dc.description.sponsorshipIdNational Natural Science Foundation of China: 61303112
dc.description.sponsorshipIdGuizhou Provincial Science and Technology Fund: [2015]2135
dc.description.sponsorshipIdFAPESP: 2013/02018-4
dc.format.extent11
dc.identifierhttp://dx.doi.org/10.3389/fphys.2016.00075
dc.identifier.citationFrontiers In Physiology. Lausanne: Frontiers Media Sa, v. 7, 11 p., 2016.
dc.identifier.doi10.3389/fphys.2016.00075
dc.identifier.fileWOS000371564800001.pdf
dc.identifier.issn1664-042X
dc.identifier.lattes7977035910952141
dc.identifier.urihttp://hdl.handle.net/11449/161271
dc.identifier.wosWOS:000371564800001
dc.language.isoeng
dc.publisherFrontiers Media Sa
dc.relation.ispartofFrontiers In Physiology
dc.rights.accessRightsAcesso aberto
dc.sourceWeb of Science
dc.subjectessential genes/proteins
dc.subjectmachine learning
dc.subjectsystems biology
dc.subjectprediction models
dc.subjectnetwork topological features
dc.titlePredicting Essential Genes and Proteins Based on Machine Learning and Network Topological Features: A Comprehensive Reviewen
dc.typeResenha
dcterms.rightsHolderFrontiers Media Sa
unesp.author.lattes7977035910952141
unesp.author.orcid0000-0001-7463-4303[3]

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