Target–Decoy MineR for determining the biological relevance of variables in noisy datasets

Machine learning algorithms excavate important variables from big data. However, deciding on the relevance of identified variables is challenging. The addition of artificial noise, 'decoy' variables, to raw data, 'target' variables, enables calculating a false-positive rate and a biological relevance probability for each variable rank. These scores allow the setting of a cut-off for informative variables, depending on the required sensitivity/specificity of a scientific question.