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Biological gene extraction path based on knowledge graph and natural language processing

The continuous progress of society and the vigorous development of science and technology have brought people the dawn of maintaining health and preventing and controlling diseases. At the same time, with the update and iteration of bioinformatics technology, the current biological gene research has...

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Detalles Bibliográficos
Autores principales: Zhang, Canlin, Cao, Xiaopei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9880067/
https://www.ncbi.nlm.nih.gov/pubmed/36712855
http://dx.doi.org/10.3389/fgene.2022.1086379
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author Zhang, Canlin
Cao, Xiaopei
author_facet Zhang, Canlin
Cao, Xiaopei
author_sort Zhang, Canlin
collection PubMed
description The continuous progress of society and the vigorous development of science and technology have brought people the dawn of maintaining health and preventing and controlling diseases. At the same time, with the update and iteration of bioinformatics technology, the current biological gene research has also undergone revolutionary changes. However, a long-standing problem in genetic research has always plagued researchers, that is, how to find the most needed sample genes from a large number of sample genes, so as to reduce unnecessary research and reduce research costs. By studying the extraction path of biological genes, it can help researchers to extract the most valuable research genes and avoid wasting time and energy. In order to solve the above problems, this paper used the Bhattacharyya distance index and the Gini index to screen the sample genes when extracting the characteristic genes of breast cancer. In the selected 49 public genes, 6 principal components were extracted by principal component analysis (PCA), and finally the experimental results were tested. It was found that when the optimal number of characteristic genes was selected as 5, the recognition rate of genes reached the highest 90.31%, which met the experimental requirements. In addition, the experiment also proved that the characteristic gene extraction method designed in this paper had a removal rate of 99.75% of redundant genes, which can greatly reduce the time and money cost of research.
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spelling pubmed-98800672023-01-28 Biological gene extraction path based on knowledge graph and natural language processing Zhang, Canlin Cao, Xiaopei Front Genet Genetics The continuous progress of society and the vigorous development of science and technology have brought people the dawn of maintaining health and preventing and controlling diseases. At the same time, with the update and iteration of bioinformatics technology, the current biological gene research has also undergone revolutionary changes. However, a long-standing problem in genetic research has always plagued researchers, that is, how to find the most needed sample genes from a large number of sample genes, so as to reduce unnecessary research and reduce research costs. By studying the extraction path of biological genes, it can help researchers to extract the most valuable research genes and avoid wasting time and energy. In order to solve the above problems, this paper used the Bhattacharyya distance index and the Gini index to screen the sample genes when extracting the characteristic genes of breast cancer. In the selected 49 public genes, 6 principal components were extracted by principal component analysis (PCA), and finally the experimental results were tested. It was found that when the optimal number of characteristic genes was selected as 5, the recognition rate of genes reached the highest 90.31%, which met the experimental requirements. In addition, the experiment also proved that the characteristic gene extraction method designed in this paper had a removal rate of 99.75% of redundant genes, which can greatly reduce the time and money cost of research. Frontiers Media S.A. 2023-01-13 /pmc/articles/PMC9880067/ /pubmed/36712855 http://dx.doi.org/10.3389/fgene.2022.1086379 Text en Copyright © 2023 Zhang and Cao. https://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) and the copyright owner(s) 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 Genetics
Zhang, Canlin
Cao, Xiaopei
Biological gene extraction path based on knowledge graph and natural language processing
title Biological gene extraction path based on knowledge graph and natural language processing
title_full Biological gene extraction path based on knowledge graph and natural language processing
title_fullStr Biological gene extraction path based on knowledge graph and natural language processing
title_full_unstemmed Biological gene extraction path based on knowledge graph and natural language processing
title_short Biological gene extraction path based on knowledge graph and natural language processing
title_sort biological gene extraction path based on knowledge graph and natural language processing
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9880067/
https://www.ncbi.nlm.nih.gov/pubmed/36712855
http://dx.doi.org/10.3389/fgene.2022.1086379
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