Ranking labs-of-origin for genetically engineered DNA using Metric Learning
This addresses a security and attribution challenge in genetic engineering, but appears incremental as it builds on existing competition frameworks.
The paper tackles the problem of identifying the lab-of-origin for genetically engineered DNA sequences by proposing a method that ranks likely labs and generates embeddings for DNA sequences and labs, outperforming classic training methods.
With the constant advancements of genetic engineering, a common concern is to be able to identify the lab-of-origin of genetically engineered DNA sequences. For that reason, AltLabs has hosted the genetic Engineering Attribution Challenge to gather many teams to propose new tools to solve this problem. Here we show our proposed method to rank the most likely labs-of-origin and generate embeddings for DNA sequences and labs. These embeddings can also perform various other tasks, like clustering both DNA sequences and labs and using them as features for Machine Learning models applied to solve other problems. This work demonstrates that our method outperforms the classic training method for this task while generating other helpful information.