Harnessing deep learning for SNP-based disease prediction in genomics

This study investigates the use of deep learning models to predict disease status from single nucleotide polymorphism (SNP) data. Eight GEO datasets were processed using a consistent pipeline involving genotype encoding, data cleaning, and multiple feature selection strategies. A variety of DL architectures-including feedforward networks, autoencoders, CNNs, and RNNs-were trained and evaluated. The novelty of this work lies in the standardized preprocessing, feature selection, and model training pipeline applied across all datasets, allowing for a direct and fair comparison of model performance. Results consistently showed that feedforward networks and autoencoders performed best across most datasets. This work offers a practical approach to applying deep learning in genomics with potential for future extensions.