Research on the application of machine learning algorithms in anti-cancer drug response prediction
10.12092/j.issn.1009-2501.2025.02.006
- VernacularTitle:机器学习算法在抗肿瘤药物响应预测中的应用研究
- Author:
Yanchen TAN
1
;
Wenwen WANG
;
Jielai XIA
;
Chen LI
Author Information
1. 空军军医大学基础医学院学员五大队十七队,西安 710032,陕西
- Publication Type:Journal Article
- Keywords:
machine learning;
drug response pre-diction;
supervised learning;
precision medicine;
neural network
- From:
Chinese Journal of Clinical Pharmacology and Therapeutics
2025;30(2):200-208
- CountryChina
- Language:Chinese
-
Abstract:
With the continuous development of genomics and precision medicine,targeted therapy and immunotherapy targeting biomarkers have ush-ered in a new era of anti-tumor therapy.However,due to the heterogeneity of tumor cells and the variability of tumor microenvironment,there are still significant differences in response to the same drug even in patient populations with the same bio-marker enrichment.By combining omics data with drug sensitivity algorithms,the response of anti-tu-mor drugs can be predicted and transformed into personalized diagnosis and treatment strategies re-quired for precision medicine,which is expected to improve the effectiveness of anti-tumor drugs in clinical treatment.Currently,machine learning is one of the commonly used modeling algorithms for predicting the response of anti-tumor drugs.How-ever,due to differences in input data and algorithm construction methods,there is currently a lack of comprehensive literature review in this field.There-fore,this article provides a review of machine learning algorithms for predicting anti-tumor drug responses,summarizing publicly available cell ge-nome characterization datasets,machine learning algorithms,and evaluation indicators in drug re-sponse prediction,as well as the current situation and challenges faced in clinical applications,in or-der to provide methodological references for the main research problems and potential solutions of machine learning algorithms in the field of drug re-sponse prediction.
