Machine Learning on a Genome-wide Association Study to Predict Late Genitourinary Toxicity After Prostate Radiation Therapy

Lignende værker

• Computational methods using genome-wide association studies to predict radiotherapy complications and to identify correlative molecular processes
  af: Oh, Jung Hun, et al.
  Udgivet: (2017)
• Machine Learning and Radiogenomics: Lessons Learned and Future Directions
  af: Kang, John, et al.
  Udgivet: (2018)
• Survey of Radiation Oncologists to Assess Interest and Potential Use of a Genetic Test Predicting Susceptibility for the Development of Toxicities After Prostate Cancer Radiation Therapy
  af: Collado, Kayla, et al.
  Udgivet: (2020)
• Patterns and Predictors of Amelioration of Genitourinary Toxicity after High-Dose Intensity-Modulated Radiation Therapy for Localized Prostate Cancer: Implications for Defining Post-Radiotherapy Urinary Toxicity
  af: Ghadjar, Pirus, et al.
  Udgivet: (2013)
• Machine learning on genome-wide association studies to predict the risk of radiation-associated contralateral breast cancer in the WECARE Study
  af: Lee, Sangkyu, et al.
  Udgivet: (2020)