CRISPR-MTL
- Multi-Task Learning with DNABERT for Joint gRNA On-Target and Off-Target Prediction.
About
CRISPR-MTL fine-tunes a single shared DNABERT encoder to predict two biologically coupled properties of a CRISPR-Cas9 guide RNA at once: on-target efficiency (how well the guide cuts its intended site, a regression task) and off-target activity (whether the guide cuts unintended near-matching sites, a binary classification task). Prior work models these two problems with separate networks, yet both reflect the same underlying phenomenon (the strength and specificity of gRNA–DNA binding). This project tests whether a shared sequence representation, learned jointly, benefits both tasks, and uses Integrated Gradients to compare what each task head attends to. It was built as a focused research sprint with full ablations and an honest accounting of where the approach helps and where data scarcity limits the conclusions.