AEmiGAP: AutoEncoder-Based miRNA-Gene Association Prediction Using Deep Learning Method

MicroRNAs (miRNAs) play a crucial role in gene regulation and are strongly linked to various diseases, including cancer. This study presents AEmiGAP, an advanced deep learning model that integrates autoencoders with long short-term memory (LSTM) networks to predict miRNA-gene associations. By enhancing feature extraction through autoencoders, AEmiGAP captures intricate, latent relationships between miRNAs and genes with unprecedented accuracy, outperforming all existing models in miRNA-gene association prediction. A thoroughly curated dataset of positive and negative miRNA-gene pairs was generated using distance-based filtering methods, significantly improving the model's AUC and overall predictive accuracy. Additionally, this study proposes two case studies to highlight AEmiGAP's application: first, a top 30 list of miRNA-gene pairs with the highest predicted association scores among previously unknown pairs, and second, a list of the top 10 miRNAs strongly associated with each of five key oncogenes. These findings establish AEmiGAP as a new benchmark in miRNA-gene association prediction, with considerable potential to advance both cancer research and precision medicine.