A Novel XGBoost Method to Infer the Primary Lesion of 20 Solid Tumor Types From Gene Expression Data.

ABSTRACT:

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 combini

SUBMITTER: Chen S 

PROVIDER: S-EPMC7886791 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature