From Gene Lists to Biological Stories with Multi-Agent AI
You’ve run your experiment. Differential expression identified 500 genes. Gene Set Enrichment Analysis returned 200 significant pathways across three databases. Now what? This is where most analyses stall. Pathway names are redundant (“cell cycle,” “G2/M checkpoint,” “mitotic spindle” all capture similar biology). Literature context requires hours of PubMed searches. Connecting pathways into mechanistic narratives requires deep domain expertise. And the sheer cognitive load means interpretation is often superficial, biased toward pathways the researcher already knows. ...