@article{IR,
      author = {Karlberg, Brian and Lee, Jordan and Wong, Chris and  Stuart, Josh and Ellrott, Kyle},
      url = {http://digitalcollections.ohsu.edu/record/9633},
      title = {Machine learning for cancer subtyping: sampling effects on  predictive accuracy and feature selection},
      publisher = {Oregon Health and Science University},
      abstract = {Accurate classification of cancer subtypes based on  genomic data is a key component of precision oncology.  Subtyping allows for better alignment of therapeutics with  the specific biology of an individual patient's tumor.  Training predictive models for classification requires  collection, sequencing, and labeling of patient samples  which incurs significant cost and thus motivates an  estimation of the minimum number of samples required to  attain a particular classification accuracy. In this work,  a minimum sample size estimation strategy for machine  learning cancer subtype prediction is developed by  combining a subsampling method for learning curve  generation with an inverse power law curve fitting method.},
      number = {IR},
      doi = {https://doi.org/10.6083/g158bj19x},
      recid = {9633},
      address = {2022-04-21},
}