Matthew H. Bakalar

This paper introduces the EDEN family of foundation models for AI-programmable therapeutic design, demonstrating their ability to generate functional gene insertions, antibiotic peptides, and synthetic microbiomes.

This paper introduces a novel genome engineering method for iPSCs that enables efficient multiplex gene editing and clone selection, improving the precision and speed of iPSC-based therapies.

The paper introduces BaseData, a new biological sequence database that expands known protein diversity by over 10x using a global biodiscovery pipeline.

The paper introduces IntQuery, a deep learning model that predicts LSI off-target activity genome-wide using data from Cryptic-seq.

The paper develops and validates methods to detect off-target recombination by Large Serine Integrases, finding higher sensitivity than whole genome sequencing.