Gita Mahmoudabadi

The paper introduces TranscriptFormer, a generative model trained on 112 million cells across 12 species, demonstrating cross-species cell type classification and disease state prediction.

The paper introduces CytoType and ESM-CE, species-specific small models for cell type classification that achieve performance comparable to large foundation models with fewer parameters.

The paper uses machine learning to identify individuals based on phageprint profiles from oral microbiomes, finding stable and unique phageprints per individual.

This study builds machine learning models to predict human tissue of origin from microbiome data and shows contaminants can contribute to model performance.

This study uses targeted sequencing of oral phage communities to show that phage compositions are individual-specific, stable over time, and not strongly influenced by geography or genetics.