Target-Decoy MineR for determining the biological relevance of variables in noisy data sets

Abstract

Machine learning algorithms excavate important variables from biological big data. However, deciding on the biological relevance of identified variables is challenging. The addition of artificial noise, decoy variables, to raw data, target variables, enables calculating a false-positive rate (FPR) and a biological relevance probability (BRp) for each variable rank. These scores allow the setting of a cut-off for informative variables can be defined, depending on the required sensitivity/ specificity of a scientific question. We demonstrate the function of the Target-Decoy MineR (TDM) with synthetic data and with experimental metabolomics results. The Target-Decoy MineR is suitable for different types of quantitative data in tabular format. An implementation of the algorithm in R is freely available from https://bitbucket.org/cesaremov/targetdecoy_mining/.