Feature Selection Methods

EIR-auto-GP offers various feature selection methods for genomic prediction. Each method follows a unique workflow, as visualized and explained below.

DL Method

The DL method uses deep learning models to compute SNP attributions, followed by a Bayesian optimization loop.

DL + GWAS Method

The DL + GWAS method combines deep learning SNP attributions with GWAS p-values for feature selection.

GWAS Method

The GWAS method filters SNPs based on GWAS p-values without additional optimization.

GWAS -> DL Method

This method first filters SNPs using GWAS p-values, then applies DL-based optimization.

../_images/gwas_then_dl_method_diagram.png

GWAS + BO Method

The GWAS + BO method uses Bayesian optimization with GWAS p-values as an upper bound constraint.

None Method

The None method involves no feature selection, directly training models on the full SNP set.