Euretos Information

Euretos Disease Atlas asks causal questions to rank the full protein-coding genome against resolved organ, tissue, and cell-type biology, across 34,000 indications. Built for biotech and pharma teams making the calls that matter: target discovery, target assessment, indication expansion, and search-and-evaluation of external assets. What makes it different. Disease Atlas runs quantitative causal inference on primary biological data: Mendelian randomisation on GWAS effect sizes, perturbation magnitudes from CRISPR and LINCS, single-cell expression from CellxGene and the Human Cell Atlas, ligand-receptor structure, and protein-protein interaction networks. No literature in the ranking layer. No language models in the reasoning path. Every score traces to a primary measurement; every claim grades against a transparent evidence tier. Calibrated per axis. Each target-disease pair carries three calibrated probabilities, one for causal biology, one for clinical reach, one for approval, rather than one rolled-up number. Per-disease confidence tiers keep the long tail explicit instead of smoothed. Validation runs against leakage-clean cohorts of historical clinical readouts, so the score doesn't accidentally see what it's supposed to predict. Cell-type resolution by default. Around 2,300 cell types per disease, each shaded by causal evidence. Bulk-tissue scoring averages away the compartment biology that decides whether a target is causal or downstream noise. Disease Atlas keeps that biology visible. Who uses it. Translational researchers, target discovery and target assessment teams, computational biologists evaluating methodology, clinical development teams running indication-expansion decisions, and business development, external innovation, and search and evaluation (S&E) teams scouting external platforms. Across biotech and pharma. Try the platform at euretos.com/disease-atlas.