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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Bibliographic Details
Published inFrontiers in genetics Vol. 13; p. 1086379
Main Authors Zhang, Canlin, Cao, Xiaopei
Format Journal Article
LanguageEnglish
Published Switzerland Frontiers Media S.A 13.01.2023
Subjects
biological gene
biological gene extraction
Genetics
knowledge graph
natural language processing
path research
biological gene
biological gene extraction
path research
natural language processing
knowledge graph
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Summary: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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Edited by: Deepak Kumar Jain, Chongqing University of Posts and Telecommunications, China
This article was submitted to Computational Genomics, a section of the journal Frontiers in Genetics
Reviewed by: Lei Shi, Luliang University, China
Fenghui Dong, Nanjing Forestry University, China
Tiefeng Wu, Qingdao University of Technology, China
ISSN:1664-8021
1664-8021
DOI:10.3389/fgene.2022.1086379