Large-Scale Gastric Cancer Susceptibility Gene Identification Based on Gradient Boosting Decision Tree
The early clinical symptoms of gastric cancer are not obvious, and metastasis may have occurred at the time of treatment. Poor prognosis is one of the important reasons for the high mortality of gastric cancer. Therefore, the identification of gastric cancer-related genes can be used as relevant mar...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-01-01
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| Series: | Frontiers in Molecular Biosciences |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmolb.2021.815243/full |
| _version_ | 1818755158984622080 |
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| author | Qing Chen Ji Zhang Banghe Bao Fan Zhang Jie Zhou |
| author_facet | Qing Chen Ji Zhang Banghe Bao Fan Zhang Jie Zhou |
| author_sort | Qing Chen |
| collection | DOAJ |
| description | The early clinical symptoms of gastric cancer are not obvious, and metastasis may have occurred at the time of treatment. Poor prognosis is one of the important reasons for the high mortality of gastric cancer. Therefore, the identification of gastric cancer-related genes can be used as relevant markers for diagnosis and treatment to improve diagnosis precision and guide personalized treatment. In order to further reveal the pathogenesis of gastric cancer at the gene level, we proposed a method based on Gradient Boosting Decision Tree (GBDT) to identify the susceptible genes of gastric cancer through gene interaction network. Based on the known genes related to gastric cancer, we collected more genes which can interact with them and constructed a gene interaction network. Random Walk was used to extract network association of each gene and we used GBDT to identify the gastric cancer-related genes. To verify the AUC and AUPR of our algorithm, we implemented 10-fold cross-validation. GBDT achieved AUC as 0.89 and AUPR as 0.81. We selected four other methods to compare with GBDT and found GBDT performed best. |
| first_indexed | 2024-12-18T05:34:42Z |
| format | Article |
| id | doaj.art-c5089ae9158849218e7026e0936204ad |
| institution | Directory Open Access Journal |
| issn | 2296-889X |
| language | English |
| last_indexed | 2024-12-18T05:34:42Z |
| publishDate | 2022-01-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Molecular Biosciences |
| spelling | doaj.art-c5089ae9158849218e7026e0936204ad2022-12-21T21:19:21ZengFrontiers Media S.A.Frontiers in Molecular Biosciences2296-889X2022-01-01810.3389/fmolb.2021.815243815243Large-Scale Gastric Cancer Susceptibility Gene Identification Based on Gradient Boosting Decision TreeQing Chen0Ji Zhang1Banghe Bao2Fan Zhang3Jie Zhou4Department of Hepatobiliary Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of Hepatobiliary Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of Pathology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaWuhan Asia General Hospital, Wuhan, ChinaDepartment of Biochemistry and Molecular Biology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaThe early clinical symptoms of gastric cancer are not obvious, and metastasis may have occurred at the time of treatment. Poor prognosis is one of the important reasons for the high mortality of gastric cancer. Therefore, the identification of gastric cancer-related genes can be used as relevant markers for diagnosis and treatment to improve diagnosis precision and guide personalized treatment. In order to further reveal the pathogenesis of gastric cancer at the gene level, we proposed a method based on Gradient Boosting Decision Tree (GBDT) to identify the susceptible genes of gastric cancer through gene interaction network. Based on the known genes related to gastric cancer, we collected more genes which can interact with them and constructed a gene interaction network. Random Walk was used to extract network association of each gene and we used GBDT to identify the gastric cancer-related genes. To verify the AUC and AUPR of our algorithm, we implemented 10-fold cross-validation. GBDT achieved AUC as 0.89 and AUPR as 0.81. We selected four other methods to compare with GBDT and found GBDT performed best.https://www.frontiersin.org/articles/10.3389/fmolb.2021.815243/fullgastric cancersusceptibility genegradient boosting decision tree (GBDT)random walk (RW)gastric cancer-related genes |
| spellingShingle | Qing Chen Ji Zhang Banghe Bao Fan Zhang Jie Zhou Large-Scale Gastric Cancer Susceptibility Gene Identification Based on Gradient Boosting Decision Tree Frontiers in Molecular Biosciences gastric cancer susceptibility gene gradient boosting decision tree (GBDT) random walk (RW) gastric cancer-related genes |
| title | Large-Scale Gastric Cancer Susceptibility Gene Identification Based on Gradient Boosting Decision Tree |
| title_full | Large-Scale Gastric Cancer Susceptibility Gene Identification Based on Gradient Boosting Decision Tree |
| title_fullStr | Large-Scale Gastric Cancer Susceptibility Gene Identification Based on Gradient Boosting Decision Tree |
| title_full_unstemmed | Large-Scale Gastric Cancer Susceptibility Gene Identification Based on Gradient Boosting Decision Tree |
| title_short | Large-Scale Gastric Cancer Susceptibility Gene Identification Based on Gradient Boosting Decision Tree |
| title_sort | large scale gastric cancer susceptibility gene identification based on gradient boosting decision tree |
| topic | gastric cancer susceptibility gene gradient boosting decision tree (GBDT) random walk (RW) gastric cancer-related genes |
| url | https://www.frontiersin.org/articles/10.3389/fmolb.2021.815243/full |
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