The Cancer Dependency Map (DepMap) project has screened over 1,000 cancer cell lines with genome-scale CRISPR-Cas9 knockout, producing a public 18,000-gene by 1,000+ cell line matrix of gene effect scores. Yet translating this 432 MB matrix into actionable experimental design decisions typically requires bespoke bioinformatics.
Cancer gene research requires synthesizing evidence across multiple public databases -- CRISPR dependency screens, GWAS associations, drug targets, pathogenic variants, and tissue expression -- yet no single tool compiles this evidence into a unified, auditable score. We present GeneDossier, a deterministic compiler that integrates pre-frozen data from DepMap (CRISPR dependencies), GWAS Catalog (disease associations), Open Targets (druggability), ClinVar (pathogenic variants), and GTEx (tissue expression) for 491 cancer-relevant genes.
Large cohort studies linking diet to the gut microbiome increasingly publish public supplementary tables containing pattern-level regression coefficients and longitudinal tracking statistics, yet the raw participant data and analysis pipelines remain controlled-access. We present DietPatch, a deterministic minimal-swap compiler that converts these public supplementary tables into an executable tool: given a baseline diet and a target dietary pattern, DietPatch scores every food by its longitudinally weighted pattern evidence and proposes the smallest set of concrete substitutions that maximize target-pattern alignment.
Published transcriptomic signatures often look convincing in one study but fail across cohorts, platforms, or nuisance biology. We present an offline, self-verifying benchmark that scores 29 gene signatures across 12 frozen real GEO expression cohorts (3,003 samples, 3 microarray platforms) to determine cross-cohort durability with confounder rejection and 4 baselines.
Antimicrobial peptide discovery often rewards assay-positive hits that later fail in salt, serum, shifted pH, or liability-sensitive settings. We present a biology-first, offline workflow that ranks APD-derived peptide leads by deployability rather than activity alone and then proposes bounded rescue edits for near misses.
We present an offline, agent-executable workflow that classifies ageing, dietary restriction, and senescence-like gene signatures from vendored HAGR snapshots, then certifies whether the result remains stable under perturbation, specific against competing longevity programs, and stronger than explicit non-longevity confounder explanations. In the frozen release, all four canonical examples classify as expected, the holdout benchmark passes 3/3, and a blind panel of 12 compact public signatures is recovered exactly.
We present an offline, agent-executable workflow that classifies ageing, dietary restriction, and senescence-like gene signatures from vendored HAGR snapshots, then certifies whether the result remains stable under perturbation, specific against competing longevity programs, and stronger than explicit non-longevity confounder explanations. In the frozen release, all four canonical examples classify as expected, the holdout benchmark passes 3/3, and a blind panel of 12 compact public signatures is recovered exactly.