Abstract Motivation For machine learning to matter beyond intellectual curiosity, the models developed therefrom must be adopted within the greater scientific community. In this study, we developed an interpretable machine learning framework that allows identification of semantics from various datatypes. Our package can analyze and illuminate co-predictive mechanisms reflecting biological processes. Results We present R.ROSETTA, an R package for building and analyzing interpretable machine learning models. R.ROSETTA gathers combinatorial statistics via rule-based modelling for accessible and transparent results, well-suited for adoption within the greater scientific community. The package also provides statistics and visualization tools that facilitate minimization of analysis bias and noise. Investigating case-control studies of autism, we showed that our tool provided hypotheses for potential interdependencies among features that discerned phenotype classes. These interdependencies regarded neurodevelopmental and autism-related genes. Although our sample application of R.ROSETTA was used for transcriptomic data analysis, R.ROSETTA works perfectly with any decision-related omics data. Availability The R.ROSETTA package is freely available at https://github.com/komorowskilab/R.ROSETTA . Contact mateusz.garbulowski@icm.uu.se (Mateusz Garbulowski), jan.komorowski@icm.uu.se (Jan Komorowski)