Machine learning based androgen receptor regulatory gene-related random forest survival model for precise treatment decision in prostate cancer
Collectively, this prediction model based on AR regulatory genes holds great clinical translational potential to solve the dilemma of treatment choice and identify potential novel therapeutic targets in PCa.
Authors
- Jun Xiao (ORCID: https://orcid.org/0000-0001-9012-1110)
- Peng Zhou (ORCID: https://orcid.org/0000-0003-1686-6354)
- Junjie Chen (ORCID: https://orcid.org/0000-0002-3258-0236)
- Yanan Wang (ORCID: https://orcid.org/0000-0002-8568-8777)
- Bo Liu (ORCID: https://orcid.org/0000-0001-5118-1890)
- Chengwei Wang (ORCID: https://orcid.org/0000-0002-0942-987X)
- Kai Zeng (ORCID: https://orcid.org/0000-0002-5987-236X)
- X. Edward Guo (ORCID: https://orcid.org/0000-0003-0804-7279)
- Zhiquan Hu
- Jia Hu (ORCID: https://orcid.org/0000-0002-3270-1448)
- Qinyu Li (ORCID: https://orcid.org/0000-0003-0862-4451)
Institutions
- The Affiliated Yongchuan Hospital of Chongqing Medical University (CN)
- Tongji Hospital (CN)
- Huazhong University of Science and Technology (CN)
- Chongqing Medical University (CN)
Publication Details
- Journal
- Heliyon
- Published
- 2024-09-01
- DOI
- https://doi.org/10.1016/j.heliyon.2024.e37256
- Citations
- 6
- Primary Topic
- Prostate Cancer Treatment and Research
- Type
- article
- Field-Weighted Citation Impact
- 2.51
Funders
- National Natural Science Foundation of China