Analysis of the first Genetic Engineering Attribution Challenge
This addresses the problem of attributing engineered biological sequences to their designers, which is crucial for accountability and credit in biotechnology, representing a competitive benchmark advancement.
The paper presents results from the first Genetic Engineering Attribution Challenge, where top teams significantly improved accuracy in identifying the lab-of-origin for engineered biological sequences, with a 10 percentage point increase in top-1 and top-10 accuracy, and an ensemble model further boosted performance.
The ability to identify the designer of engineered biological sequences -- termed genetic engineering attribution (GEA) -- would help ensure due credit for biotechnological innovation, while holding designers accountable to the communities they affect. Here, we present the results of the first Genetic Engineering Attribution Challenge, a public data-science competition to advance GEA. Top-scoring teams dramatically outperformed previous models at identifying the true lab-of-origin of engineered sequences, including an increase in top-1 and top-10 accuracy of 10 percentage points. A simple ensemble of prizewinning models further increased performance. New metrics, designed to assess a model's ability to confidently exclude candidate labs, also showed major improvements, especially for the ensemble. Most winning teams adopted CNN-based machine-learning approaches; however, one team achieved very high accuracy with an extremely fast neural-network-free approach. Future work, including future competitions, should further explore a wide diversity of approaches for bringing GEA technology into practical use.