Pursuing Better Biomarkers for Immunotherapy Response in Cancer Through a Crowdsourced Data Challenge

This publication has 10 references indexed in Scilit:

• Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms
  JAMA Network Open, 2020
• Multiple Myeloma DREAM Challenge reveals epigenetic regulator PHF19 as marker of aggressive disease
  Leukemia, 2020
• Current Challenges in Cancer Immunotherapy: Multimodal Approaches to Improve Efficacy and Patient Response Rates
  Journal of Oncology, 2019
• Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response
  Nature Medicine, 2018
• Alternative models for sharing confidential biomedical data
  Nature Biotechnology, 2018
• The Immune Landscape of Cancer
  Immunity, 2018
• The application of crowdsourcing approaches to cancer research: a systematic review
  Cancer Medicine, 2017
• First-Line Nivolumab in Stage IV or Recurrent Non–Small-Cell Lung Cancer
  New England Journal of Medicine, 2017
• Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data
  The Lancet Oncology, 2017
• Crowdsourcing biomedical research: leveraging communities as innovation engines
  Nature Reviews Genetics, 2016