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Predicting Essential Genes and Proteins Based on Machine Learning and Network Topological Features: A Comprehensive Review
Essential 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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2016
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4781880/ https://www.ncbi.nlm.nih.gov/pubmed/27014079 http://dx.doi.org/10.3389/fphys.2016.00075 |
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author | Zhang, Xue Acencio, Marcio Luis Lemke, Ney |
author_facet | Zhang, Xue Acencio, Marcio Luis Lemke, Ney |
author_sort | Zhang, Xue |
collection | PubMed |
description | Essential 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. |
format | Online Article Text |
id | pubmed-4781880 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-47818802016-03-24 Predicting Essential Genes and Proteins Based on Machine Learning and Network Topological Features: A Comprehensive Review Zhang, Xue Acencio, Marcio Luis Lemke, Ney Front Physiol Physiology Essential 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. Frontiers Media S.A. 2016-03-08 /pmc/articles/PMC4781880/ /pubmed/27014079 http://dx.doi.org/10.3389/fphys.2016.00075 Text en Copyright © 2016 Zhang, Acencio and Lemke. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Physiology Zhang, Xue Acencio, Marcio Luis Lemke, Ney Predicting Essential Genes and Proteins Based on Machine Learning and Network Topological Features: A Comprehensive Review |
title | Predicting Essential Genes and Proteins Based on Machine Learning and Network Topological Features: A Comprehensive Review |
title_full | Predicting Essential Genes and Proteins Based on Machine Learning and Network Topological Features: A Comprehensive Review |
title_fullStr | Predicting Essential Genes and Proteins Based on Machine Learning and Network Topological Features: A Comprehensive Review |
title_full_unstemmed | Predicting Essential Genes and Proteins Based on Machine Learning and Network Topological Features: A Comprehensive Review |
title_short | Predicting Essential Genes and Proteins Based on Machine Learning and Network Topological Features: A Comprehensive Review |
title_sort | predicting essential genes and proteins based on machine learning and network topological features: a comprehensive review |
topic | Physiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4781880/ https://www.ncbi.nlm.nih.gov/pubmed/27014079 http://dx.doi.org/10.3389/fphys.2016.00075 |
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