# VenusGenome

A microbial gene's function depends on the protein it encodes and on where that gene sits and what it neighbors, and encoding proteins one at a time discards that organization. VenusGenome takes frozen protein embeddings as gene inputs and uses a 75% masking autoencoder that forces reconstruction from sparse genomic context, with attention that separates within-contig from cross-contig relationships and encodes relative strand orientation. Pretrained on more than 1.7 million microbial genome assemblies, the frozen encoder reaches the highest mean performance among evaluated models on nine of eleven primary metrics, with zero-shot PPI genome-macro Spearman correlation of 0.300 versus 0.201 for BacFormer.

A microbial gene's function depends on the protein it encodes and on where that gene sits and what it neighbors, and encoding proteins one at a time discards that organization. VenusGenome takes frozen protein embeddings as gene inputs and uses a 75% masking autoencoder that forces reconstruction from sparse genomic context, with attention that separates within-contig from cross-contig relationships and encodes relative strand orientation. Pretrained on more than 1.7 million microbial genome assemblies, the frozen encoder reaches the highest mean performance among evaluated models on nine of eleven primary metrics, with zero-shot PPI genome-macro Spearman correlation of 0.300 versus 0.201 for BacFormer.