Semi-supervised learning improves gene expression-based prediction of cancer recurrence

@article{Shi2011SemisupervisedLI,
  title={Semi-supervised learning improves gene expression-based prediction of cancer recurrence},
  author={Mingguang Shi and Bing Zhang},
  journal={Bioinformatics},
  year={2011},
  volume={27 21},
  pages={3017-23}
}
MOTIVATION Gene expression profiling has shown great potential in outcome prediction for different types of cancers. Nevertheless, small sample size remains a bottleneck in obtaining robust and accurate classifiers. Traditional supervised learning techniques can only work with labeled data. Consequently, a large number of microarray data that do not have sufficient follow-up information are disregarded. To fully leverage all of the precious data in public databases, we turned to a semi… CONTINUE READING
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