A big-data algorithm in Python that processes the genetic-interaction data of Saccharomyces cerevisiae with a Mixed-Membership Stochastic Block Model (MMSBM) and predicts the likelihood of non-observed interactions between triplets of genes. It also runs additional validation of the fitness of the model against the dataset and offers some data visualization of the results. It started as my Bioinformatics final degree project in the SEES:lab research group (Sales-Pardo & Guimerà) and later contributed to a 2023 PNAS publication on hyperedge prediction in complex networks.