R.ROSETTA: an interpretable machine learning framework

Abstract ROSETTA is a rough set-based classification toolkit that aims at identifying semantics from various data types. Here we present the R.ROSETTA package, which is an R wrapper of ROSETTA. The package significantly enhances the accessibility of the existing machine learning environment and the interpretability of the results. The ROSETTA functions have been enriched and improved by the incorporation of novel components targeting bioinformatics applications. Such improvements include: undersampling imbalanced datasets, estimation of the statistical significance of classification rules, retrieval of support sets, prediction of external data and integration with rule visualization frameworks. We tested the performance of R.ROSETTA on a complex dataset involving gene expression measurements for autistic and non-autistic young males. We demonstrated that R.ROSETTA facilitated the detection of novel gene-gene interactions. The results demonstrated the potential of R.ROSETTA classifiers to identify putative biomarkers and novel biological interactions.