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Research on Cancer Molecular Typing Based on High-Throughput Sequencing Technology

This paper studies the role of high-throughput measurement technology in cancer molecular typing. Based on the Dendrix algorithm, the model proposed in this paper selects the gene replication time as an inherent attribute that affects the frequency of gene mutations and adds it to the model. After s...

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Detalles Bibliográficos
Autor principal: Wei, Dan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9013290/
https://www.ncbi.nlm.nih.gov/pubmed/35437445
http://dx.doi.org/10.1155/2021/9941475
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author Wei, Dan
author_facet Wei, Dan
author_sort Wei, Dan
collection PubMed
description This paper studies the role of high-throughput measurement technology in cancer molecular typing. Based on the Dendrix algorithm, the model proposed in this paper selects the gene replication time as an inherent attribute that affects the frequency of gene mutations and adds it to the model. After setting the size of the gene set, compared with the Dendrix algorithm, the model does not need to delete the gene set that has been found in the process of searching the pathway, and it can find more driving pathway gene sets. Based on the high coverage and high exclusivity of the driving gene set in the pathway and the influence of gene covariates, this paper constructs an adaptive multiobjective optimization model. In order to overcome the problem of gene mutation heterogeneity, this model introduces gene covariates as the weight of gene mutation frequency so that the model is adaptive to each gene. The analysis of the research results shows the reliability of high-throughput sequencing technology.
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spelling pubmed-90132902022-04-17 Research on Cancer Molecular Typing Based on High-Throughput Sequencing Technology Wei, Dan Comput Math Methods Med Research Article This paper studies the role of high-throughput measurement technology in cancer molecular typing. Based on the Dendrix algorithm, the model proposed in this paper selects the gene replication time as an inherent attribute that affects the frequency of gene mutations and adds it to the model. After setting the size of the gene set, compared with the Dendrix algorithm, the model does not need to delete the gene set that has been found in the process of searching the pathway, and it can find more driving pathway gene sets. Based on the high coverage and high exclusivity of the driving gene set in the pathway and the influence of gene covariates, this paper constructs an adaptive multiobjective optimization model. In order to overcome the problem of gene mutation heterogeneity, this model introduces gene covariates as the weight of gene mutation frequency so that the model is adaptive to each gene. The analysis of the research results shows the reliability of high-throughput sequencing technology. Hindawi 2021-12-07 /pmc/articles/PMC9013290/ /pubmed/35437445 http://dx.doi.org/10.1155/2021/9941475 Text en Copyright © 2021 Dan Wei. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Wei, Dan
Research on Cancer Molecular Typing Based on High-Throughput Sequencing Technology
title Research on Cancer Molecular Typing Based on High-Throughput Sequencing Technology
title_full Research on Cancer Molecular Typing Based on High-Throughput Sequencing Technology
title_fullStr Research on Cancer Molecular Typing Based on High-Throughput Sequencing Technology
title_full_unstemmed Research on Cancer Molecular Typing Based on High-Throughput Sequencing Technology
title_short Research on Cancer Molecular Typing Based on High-Throughput Sequencing Technology
title_sort research on cancer molecular typing based on high-throughput sequencing technology
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9013290/
https://www.ncbi.nlm.nih.gov/pubmed/35437445
http://dx.doi.org/10.1155/2021/9941475
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