NCI-CPTAC Multi-omics Enabled Sample Mislabeling Correction Challenge

Prize: Prize (including medals and awards)


Sample mislabeling (accidental swapping of patient samples) or data mislabeling (accidental swapping of patient omics data) is known to be one of the obstacles in basic and translational research because this accidental swapping contributes to irreproducible results and invalid conclusions. The objective of this challenge is to encourage development and evaluation of computational algorithms that can accurately detect and correct mislabeled samples using rich multi-omics datasets.
Degree of recognitionInternational
Granting OrganisationsNational Cancer Institute, NCI

Free keywords

  • Machine learning
  • multi-omics
  • mislabeling correction