Identification of radiation-sensitive genes using machine learning algorithms
10.13491/j.issn.1004-714X.2026.02.013
- VernacularTitle:基于机器学习算法的辐射敏感基因筛选策略建立及初步验证
- Author:
Yizhe GAO
1
;
Tianjing CAI
1
;
Shuang LI
1
;
Xuelei TIAN
1
;
Cong XI
1
;
Juan YAN
1
;
Qingjie LIU
1
Author Information
1. National Institute for Radiological Protection, China CDC, Key Laboratory of Radiological Protection and Nuclear Emergency, Chinese Center for Disease Control and Prevention, Beijing 100088, China.
- Publication Type:OriginalArticles
- Keywords:
Ionizing radiation;
Transcriptomics;
Biomarkers;
Machine learning;
Strategy modeling
- From:
Chinese Journal of Radiological Health
2026;35(2):240-245
- CountryChina
- Language:Chinese
-
Abstract:
Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.
