iSnoDi-LSGT: identifying snoRNA-disease associations based on local similarity constraint and global topological constraint

Abstract

Growing evidences prove that small nucleolar RNAs (snoRNAs) have important functions in various biological processes, malfunction of which leads to the emergence and development of complex diseases. However, identifying snoRNA-disease associations is an ongoing challenging task due to the considerable time-consuming and money-consuming for biological experiments. Therefore, it is urgent to design efficient and economical methods for the identification of snoRNA-diseases associations. In this regard, we propose a computational method named iSnoDi-LSGT, which utilizes snoRNA sequence similarity and disease similarity as local similarity constraint. The iSnoDi-LSGT predictor further employs network embedding technology to extract topological features of snoRNAs and diseases, based on which snoRNA topological similarity and disease topological similarity are calculated as global topological constraint. To the best knowledge of ours, iSnoDi-LSGT is the first computational method for snoRNA-disease association identification. The experimental results indicate that iSnoDi-LSGT predictor can effectively predict the unknown snoRNA-disease associations. The web server of iSnoDi-LSGT predictor is freely available at http://bliulab.net/iSnoDi-LSGT/.