Synthetic lethality occurs when simultaneous loss of two genes is lethal while loss of either alone is tolerated, providing a therapeutic strategy to exploit cancer-specific vulnerabilities. We present SyntheticLethalityEngine, a pure-Python pipeline for synthetic lethality analysis.
Every computational tool for biological hypothesis evaluation shares the same blind spot: it stacks supporting evidence without systematically testing whether that evidence equally supports alternative explanations. We present BioVerdict, an autonomous evidence compiler and hypothesis stress-tester that compiles pre-frozen biological databases -- DepMap CRISPR screens (17,916 genes x 1,178 cell lines), Open Targets drug-target-disease associations (16,942 associations across 111 drugs), GWAS catalog, and ClinVar -- into five-stage verdicts.