Integrative machine learning approach for forecasting lung cancer chemosensitivity: From algorithm to cell line validation
This study successfully developed a high-performance machine learning model for predicting chemotherapy response in lung cancer and elucidated a strong correlation between TMED4 and DYNLRB1 gene expression and chemotherapy resistance. We further provide a user-friendly web server (available at https://smuonco.shinyapps.io/LC-DrugPortal/) to enable clinical utilization of our model, promoting personalized chemotherapy selection for lung cancer patients.
Authors
- Haoxuan Ying (ORCID: https://orcid.org/0000-0002-0957-9827)
- Peng Luo (ORCID: https://orcid.org/0000-0002-8215-2045)
- Chunqian Yang
- Ting Wei (ORCID: https://orcid.org/0000-0001-7840-1013)
- Anqi Lin (ORCID: https://orcid.org/0000-0002-6324-0410)
- Jian Zhang (ORCID: https://orcid.org/0000-0001-7217-0111)
- Jinghong Chen
- Yonglin Yi
Institutions
- Zhujiang Hospital (CN)
- Southern Medical University (CN)
- University of Hong Kong (HK)
Publication Details
- Journal
- Computational and Structural Biotechnology Journal
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1016/j.csbj.2025.07.043
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00