Quaid Morris

Abstract In the face of rapidly accumulating genomic data, our ability to predict key mature RNA properties that underlie transcript function and regulation remains limited. Pretrained genomic foundation models offer an avenue to adapt learned RNA representations to biological prediction tasks; however, existing models are trained using strategies borrowed from textual domains that do not leverage biological domain knowledge.

Abstract Cancers differ in how they spread. These routes of metastatic dissemination can be reconstructed from tumor sequencing data, but current reconstruction methods scale poorly or rely on assumptions that do not reflect known biology. Metient overcomes these limitations using gradient-based, multiobjective optimization to generate multiple hypotheses of metastatic spread that are rescored using independent genetic distance and organotropism data.