Rapid learning for precision oncology

@article{Shrager2014RapidLF,
  title={Rapid learning for precision oncology},
  author={Jeff Shrager and Jay M. Tenenbaum},
  journal={Nature Reviews Clinical Oncology},
  year={2014},
  volume={11},
  pages={109-118}
}
The emerging paradigm of Precision Oncology 3.0 uses panomics and sophisticated methods of statistical reverse engineering to hypothesize the putative networks that drive a given patient's tumour, and to attack these drivers with combinations of targeted therapies. Here, we review a paradigm termed Rapid Learning Precision Oncology wherein every treatment event is considered as a probe that simultaneously treats the patient and provides an opportunity to validate and refine the models on which… 
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