LDAGSO: Predicting IncRNA-Disease Associations from Graph Sequences and Disease Ontology via Deep Learning techniques

Recent studies have confirmed the significant effects of long non-coding RNAs (IncRNAs) in understanding the mechanism of diseases. Because of the relatively small number of validated associations between IncRNAs and diseases, and previous computational methods have limited performance without capturing important features of sequences and ontology information, we developed LDAGSO, a novel deep learning framework to predict IncRNA and disease associations from IncRNA sequences and disease ontology. For IncRNA sequences, we converted them into graph structure based on k-mer technique and de Bruijn graph, and captured high-level features of the graph using graph convolutional networks. For diseases, we extracted ontology term paths from the disease ontology tree, and treated them as sentences to obtain their feature representation using Bidirectional Encoder Representations from Transformers (BERT) technique. Finally, these two kinds of features were fed into a fully connected layer to perform the task of association prediction between 1ncRNAs and diseases. According to the results, our approach provides state-of-the-art results when evaluated by leave-one-out cross-validation.