Current integrative prioritization methods use either functional data (L2G, cS2G) or network convergence of GWAS signal (PoPS).
FLAMES combines both, by combining XGBoost predictions using functional evidence in the GWAS locus with PoPS predictions.
Current integrative prioritization methods use either functional data (L2G, cS2G) or network convergence of GWAS signal (PoPS).
FLAMES combines both, by combining XGBoost predictions using functional evidence in the GWAS locus with PoPS predictions.