• Corpus ID: 220686387

Outcome-Guided Disease Subtyping for High-Dimensional Omics Data

@article{Liu2020OutcomeGuidedDS,
  title={Outcome-Guided Disease Subtyping for High-Dimensional Omics Data},
  author={Peng Liu and Yusi Fang and Zhao Ren and Lu Tang and George C. Tseng},
  journal={arXiv: Methodology},
  year={2020}
}
High-throughput microarray and sequencing technology have been used to identify disease subtypes that could not be observed otherwise by using clinical variables alone. The classical unsupervised clustering strategy concerns primarily the identification of subpopulations that have similar patterns in gene features. However, as the features corresponding to irrelevant confounders (e.g. gender or age) may dominate the clustering process, the resulting clusters may or may not capture clinically… 

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