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A Novel XGBoost Method to Infer the Primary Lesion of 20 Solid Tumor Types From Gene Expression Data
PURPOSE: Establish a suitable machine learning model to identify its primary lesions for primary metastatic tumors in an integrated learning approach, making it more accurate to improve primary lesions’ diagnostic efficiency. METHODS: After deleting the features whose expression level is lower than...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7886791/ https://www.ncbi.nlm.nih.gov/pubmed/33613644 http://dx.doi.org/10.3389/fgene.2021.632761 |
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author | Chen, Sijie Zhou, Wenjing Tu, Jinghui Li, Jian Wang, Bo Mo, Xiaofei Tian, Geng Lv, Kebo Huang, Zhijian |
author_facet | Chen, Sijie Zhou, Wenjing Tu, Jinghui Li, Jian Wang, Bo Mo, Xiaofei Tian, Geng Lv, Kebo Huang, Zhijian |
author_sort | Chen, Sijie |
collection | PubMed |
description | PURPOSE: Establish a suitable machine learning model to identify its primary lesions for primary metastatic tumors in an integrated learning approach, making it more accurate to improve primary lesions’ diagnostic efficiency. METHODS: After deleting the features whose expression level is lower than the threshold, we use two methods to perform feature selection and use XGBoost for classification. After the optimal model is selected through 10-fold cross-validation, it is verified on an independent test set. RESULTS: Selecting features with around 800 genes for training, the R(2)-score of a 10-fold CV of training data can reach 96.38%, and the R(2)-score of test data can reach 83.3%. CONCLUSION: These findings suggest that by combining tumor data with machine learning methods, each cancer has its corresponding classification accuracy, which can be used to predict primary metastatic tumors’ location. The machine-learning-based method can be used as an orthogonal diagnostic method to judge the machine learning model processing and clinical actual pathological conditions. |
format | Online Article Text |
id | pubmed-7886791 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-78867912021-02-18 A Novel XGBoost Method to Infer the Primary Lesion of 20 Solid Tumor Types From Gene Expression Data Chen, Sijie Zhou, Wenjing Tu, Jinghui Li, Jian Wang, Bo Mo, Xiaofei Tian, Geng Lv, Kebo Huang, Zhijian Front Genet Genetics PURPOSE: Establish a suitable machine learning model to identify its primary lesions for primary metastatic tumors in an integrated learning approach, making it more accurate to improve primary lesions’ diagnostic efficiency. METHODS: After deleting the features whose expression level is lower than the threshold, we use two methods to perform feature selection and use XGBoost for classification. After the optimal model is selected through 10-fold cross-validation, it is verified on an independent test set. RESULTS: Selecting features with around 800 genes for training, the R(2)-score of a 10-fold CV of training data can reach 96.38%, and the R(2)-score of test data can reach 83.3%. CONCLUSION: These findings suggest that by combining tumor data with machine learning methods, each cancer has its corresponding classification accuracy, which can be used to predict primary metastatic tumors’ location. The machine-learning-based method can be used as an orthogonal diagnostic method to judge the machine learning model processing and clinical actual pathological conditions. Frontiers Media S.A. 2021-02-03 /pmc/articles/PMC7886791/ /pubmed/33613644 http://dx.doi.org/10.3389/fgene.2021.632761 Text en Copyright © 2021 Chen, Zhou, Tu, Li, Wang, Mo, Tian, Lv and Huang. http://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 Chen, Sijie Zhou, Wenjing Tu, Jinghui Li, Jian Wang, Bo Mo, Xiaofei Tian, Geng Lv, Kebo Huang, Zhijian A Novel XGBoost Method to Infer the Primary Lesion of 20 Solid Tumor Types From Gene Expression Data |
title | A Novel XGBoost Method to Infer the Primary Lesion of 20 Solid Tumor Types From Gene Expression Data |
title_full | A Novel XGBoost Method to Infer the Primary Lesion of 20 Solid Tumor Types From Gene Expression Data |
title_fullStr | A Novel XGBoost Method to Infer the Primary Lesion of 20 Solid Tumor Types From Gene Expression Data |
title_full_unstemmed | A Novel XGBoost Method to Infer the Primary Lesion of 20 Solid Tumor Types From Gene Expression Data |
title_short | A Novel XGBoost Method to Infer the Primary Lesion of 20 Solid Tumor Types From Gene Expression Data |
title_sort | novel xgboost method to infer the primary lesion of 20 solid tumor types from gene expression data |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7886791/ https://www.ncbi.nlm.nih.gov/pubmed/33613644 http://dx.doi.org/10.3389/fgene.2021.632761 |
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