A Novel XGBoost Method to Identify Cancer Tissue-of-Origin Based on Copy Number Variations.

ABSTRACT: 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 achie

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC7716814 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature