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 (Frontiers in Genetics, 2021)
Purpose: Establish a suitable machine learning model to identify its primary lesions for primary metastatic tumors in an integrated learning manner, making it more accurate to improve the diagnostic efficiency of primary lesions. Methods: After deleting the features whose expression level is lower than the threshold, we use two methods to perform feature selection respectively 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 -score of 10-cv of training data can reach 96.38%, and the -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 the location of primary metastatic tumors. 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.