Fig. 1: Overview of transfer learning framework for enhancing SNP discovery in GWAS.
From: Transformer-based deep learning enhances discovery in migraine GWAS
Training and Inference Pipeline (left panel): The pipeline includes training and inference stages. A baseline model is trained using MDD GWAS data and fine-tuned with migraine GWAS data through transfer learning. The fine-tuned model is then used to identify enhanced SNPs in the migraine GWAS dataset. Model Architecture (right panel): The model architecture is based on a Transformer encoder, designed to capture nonlinear relationships among GWAS features. The architecture includes input embedding, positional encoding, and multi-head attention layers, followed by a fully connected layer with a sigmoid activation function for binary classification of variant associations. Source: The MDD and migraine icons are from vecteezy.com, available under a Free License.
