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Study of biological networks using graph theory
As an effective modeling, analysis and computational tool, graph theory is widely used in biological mathematics to deal with various biology problems. In the field of microbiology, graph can express the molecular structure, where cell, gene or protein can be denoted as a vertex, and the connect ele...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6117245/ https://www.ncbi.nlm.nih.gov/pubmed/30174525 http://dx.doi.org/10.1016/j.sjbs.2017.11.022 |
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author | Gao, Wei Wu, Hualong Siddiqui, Muhammad Kamran Baig, Abdul Qudair |
author_facet | Gao, Wei Wu, Hualong Siddiqui, Muhammad Kamran Baig, Abdul Qudair |
author_sort | Gao, Wei |
collection | PubMed |
description | As an effective modeling, analysis and computational tool, graph theory is widely used in biological mathematics to deal with various biology problems. In the field of microbiology, graph can express the molecular structure, where cell, gene or protein can be denoted as a vertex, and the connect element can be regarded as an edge. In this way, the biological activity characteristic can be measured via topological index computing in the corresponding graphs. In our article, we mainly study the biology features of biological networks in terms of eccentric topological indices computation. By means of graph structure analysis and distance calculating, the exact expression of several important eccentric related indices of hypertree network and X-tree are determined. The conclusions we get in this paper illustrate that the bioengineering has the promising application prospects. |
format | Online Article Text |
id | pubmed-6117245 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-61172452018-08-31 Study of biological networks using graph theory Gao, Wei Wu, Hualong Siddiqui, Muhammad Kamran Baig, Abdul Qudair Saudi J Biol Sci Article As an effective modeling, analysis and computational tool, graph theory is widely used in biological mathematics to deal with various biology problems. In the field of microbiology, graph can express the molecular structure, where cell, gene or protein can be denoted as a vertex, and the connect element can be regarded as an edge. In this way, the biological activity characteristic can be measured via topological index computing in the corresponding graphs. In our article, we mainly study the biology features of biological networks in terms of eccentric topological indices computation. By means of graph structure analysis and distance calculating, the exact expression of several important eccentric related indices of hypertree network and X-tree are determined. The conclusions we get in this paper illustrate that the bioengineering has the promising application prospects. Elsevier 2018-09 2017-11-14 /pmc/articles/PMC6117245/ /pubmed/30174525 http://dx.doi.org/10.1016/j.sjbs.2017.11.022 Text en © 2017 Production and hosting by Elsevier B.V. on behalf of King Saud University. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Article Gao, Wei Wu, Hualong Siddiqui, Muhammad Kamran Baig, Abdul Qudair Study of biological networks using graph theory |
title | Study of biological networks using graph theory |
title_full | Study of biological networks using graph theory |
title_fullStr | Study of biological networks using graph theory |
title_full_unstemmed | Study of biological networks using graph theory |
title_short | Study of biological networks using graph theory |
title_sort | study of biological networks using graph theory |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6117245/ https://www.ncbi.nlm.nih.gov/pubmed/30174525 http://dx.doi.org/10.1016/j.sjbs.2017.11.022 |
work_keys_str_mv | AT gaowei studyofbiologicalnetworksusinggraphtheory AT wuhualong studyofbiologicalnetworksusinggraphtheory AT siddiquimuhammadkamran studyofbiologicalnetworksusinggraphtheory AT baigabdulqudair studyofbiologicalnetworksusinggraphtheory |