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A Novel XGBoost Method to Identify Cancer Tissue-of-Origin Based on Copy Number Variations by Zhang, Yulin; Feng, Tong; Wang, Shudong; Dong, Ruyi; Yang, Jialiang; Su, Jionglong; Wang, Bo is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is A Novel XGBoost Method to Identify Cancer Tissue-of-Origin Based on Copy Number Variations about?

The discovery of cancer of unknown primary (CUP) is of great significance in designing more effective treatments and improving the diagnostic efficiency in cancer patients. In the study, we develop an appropriate machine learning model for tracing the tissue of origin of CUP with high accuracy after feature engineering and model evaluation. Based on a copy number variation data consisting of 4,566 training cases and 1,262 independent validation cases, an XGBoost classifier is applied to 10 types of cancer. Extremely randomized tree (Extra tree) is used for dimension reduction so that fewer variables replace the original high-dimensional variables. Features with top 300 weights are selected and principal component analysis is applied to eliminate noise. We find that XGBoost classifier achieves the highest overall accuracy of 0.8913 in the 10-fold cross-validation for training samples and 0.7421 on independent validation datasets for predicting tumor tissue of origin. Furthermore, by contrasting various performance indices, such as precision and recall rate, the experimental results show that XGBoost classifier significantly improves the classification performance of various tumors w

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Author

Zhang, Yulin; Feng, Tong; Wang, Shudong; Dong, Ruyi; Yang, Jialiang; Su, Jionglong; Wang, Bo

Publisher

Frontiers; Frontiers Media SA; Frontiers Media S.A.; Lausanne: Frontiers Media S.A., 2010- (ISSN 1664-8021)

Published

2020

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)