This paper is in the following e-collection/theme issue:

Digital Health Reviews (1315) Precision Medicine (78) Clinical Information and Decision Making (1423) Machine Learning (1488) Formative Evaluation of Digital Health Interventions (2466)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/48527, first published .
Predictive Value of Machine Learning for Platinum Chemotherapy Responses in Ovarian Cancer: Systematic Review and Meta-Analysis

Predictive Value of Machine Learning for Platinum Chemotherapy Responses in Ovarian Cancer: Systematic Review and Meta-Analysis

Predictive Value of Machine Learning for Platinum Chemotherapy Responses in Ovarian Cancer: Systematic Review and Meta-Analysis

Authors of this article:

Qingyi Wang1 Author Orcid Image ;   Zhuo Chang2 Author Orcid Image ;   Xiaofang Liu1 Author Orcid Image ;   Yunrui Wang1 Author Orcid Image ;   Chuwen Feng1 Author Orcid Image ;   Yunlu Ping1 Author Orcid Image ;   Xiaoling Feng3 Author Orcid Image
Article Authors Cited by (4) Tweetations (3) Metrics

Journals

  1. Wei J, Wang M, Wu Y. A disulfidptosis-related lncRNAs cluster to forecast the prognosis and immune landscapes of ovarian cancer. Frontiers in Genetics 2024;15 View
  2. Zhang C, Yang J, Chen S, Sun L, Li K, Lai G, Peng B, Zhong X, Xie B. Artificial intelligence in ovarian cancer drug resistance advanced 3PM approach: subtype classification and prognostic modeling. EPMA Journal 2024;15(3):525 View
  3. Avci C, Bagca B, Shademan B, Takanlou L, Takanlou M, Nourazarian A. Machine learning in oncological pharmacogenomics: advancing personalized chemotherapy. Functional & Integrative Genomics 2024;24(5) View
  4. Lee Z, Cai J, Wang L, Yang M. Ensemble Algorithm Based on Gene Selection, Data Augmentation, and Boosting Approaches for Ovarian Cancer Classification. Diagnostics 2024;14(24):2772 View