Overview
Machine learning is transforming synthetic biology by enabling predictive design to replace traditional trial-and-error approaches. Advanced and efficient machine learning models can generate accurate predictions that accelerate laboratory workflows, advance research, and reduce experimental costs. However, model performance critically depends on training data quality, making extensive preprocessing essential to ensure models learn from rich, high-quality datasets.
In siREN, our machine learning models provided initial predictions of candidate siRNA silencing efficacy before experimental validation. To support model development, we created siRBench, a harmonized dataset containing more than 4000 siRNAs with experimentally measured silencing efficiencies and complementary thermodynamic and structural features.