Andre Cornman

This paper uses a genomic language model to design and optimize a synthetic biosynthetic pathway for producing a non-natural molecule, achieving a 9.4-fold increase in production.

The paper identifies microbial intergenic features using a sparse autoencoder and multimodal search, revealing previously uncharacterized regulatory DNA and structured RNA elements.

FlashPPI predicts proteome-scale microbial protein interactions in linear time using contrastive learning and genomic language models, outperforming existing methods and enabling rapid network analysis.

NPannotator automates the elucidation of type I polyketide synthase pathways by linking genomic data with chemical structures to infer gene order and substrate specificity.

Gaia enables context-aware protein sequence search in microbial genomes by integrating sequence, structure, and genomic context information, improving annotation of functionally related genes.

The Open MetaGenomic (OMG) corpus combines 3.1T base pairs and 3.3B protein coding sequences in mixed amino-acid and nucleotide format to pretrain the genomic language model gLM2.

The paper introduces DGEB, a benchmark for evaluating genomic embeddings across diverse sequences and functions, revealing performance limitations in existing models.

This paper trains a genomic language model on metagenomic data to predict protein co-regulation and function by learning latent relationships in genomic context.