Benchmarking, Reproducibility, and FAIR Pipelines for RNA-RNA Interaction Prediction

The rapid growth of machine learning and deep learning methods for RNA-RNA interaction (RRI) prediction has led to impressive gains in predictive performance. However, the lack of standardized benchmarking practices, reproducibility, and transparent data handling has made it difficult to fairly compare models and assess their true biological utility. Variability in datasets, evaluation protocols, negative sampling strategies, and reporting practices often results in inflated or noncomparable performance claims. This chapter addresses these challenges by presenting best practices for benchmarking RRI prediction models, ensuring reproducibility, and developing FAIR (Findable, Accessible, Interoperable, Reusable) computational pipelines. We discuss dataset construction and leakage issues, evaluation metrics, cross-dataset validation, reproducible workflows using containerization and workflow managers, and community-driven benchmarking initiatives. Together, these practices provide a foundation for robust, transparent, and reusable RRI research.