Modern molecular datasets contain thousands of variables, but only a small subset truly drives disease behavior.

MFeaST identifies those critical drivers.

By applying an ensemble of statistical and machine-learning models, MFeaST isolates biologically meaningful features from high-dimensional data while highlighting clinical signals and reducing noise.