Predicting microRNA-disease association by autoencoder and extreme gradient boosting

Last few years, numerous related reaserches have demonstrated that microRNAs (miRNAs) influenced the evolution and advancement of intricate human illnesses, so observing associations of miRNA-disease can contribute to exploring and treating these diseases. However, based on biological experiments, it is frequently observed that the experimentation process tends to be costly and time-intensive, often performed on a small-scale. Therefore, the development of multiple algorithms plays an important role in predicting the potential association of mirnas with disease. In the research, we presented a framework of computation that combined autoencoder with extreme gradient boosting to predict unknown miRNAdisease association (AEXGB). Firstly, the similarity between miRNA and various diseases is synthesized, and representative disease similarity and miRNA similarity can be constructed, respectively. In addition, disease similarity and miRNA similarity can be combined, and the original features of mirNA-disease pairs can be constructed. These features were send as in put to autoencoder (AE) for extracting hidden biological patterns. Unverified associations dependent on IRNA-disease for extracted deep features can be inferred by extreme gradient enhancement (XGBoost). To enable methods to be evaluated, case studies and cross-validation experiments can be used, and the effectiveness of AEXGB in observing potential mirNA-disease associations can be observed.