Area of research
Renewable Energy, Sustainability and the Environment · Electronic, Optical and Magnetic Materials
Research interest
Research topics from publications: DAE-CFR: detecting microRNA-disease associations using deep autoencoder and combined feature representation. Representative work: BACKGROUND: MicroRNA (miRNA) has been shown to play a key role in the occurrence and progression of diseases, making uncovering miRNA-disease associations vital for disease prevention and therapy. However, traditional laboratory methods for detecting these associations are slow, strenuous, expensive, and uncertain. Although numerous advanced algorithms have emerged, it is still a challenge to develop more effective methods to explore underlying miRNA-disease associations. RESULTS: In the study, we designed a novel approach on the basis of deep autoencoder and combined feature representation (DAE-CFR) to predict possible miRNA-disease associations. We began by creating integrated similarity m