DeepIDA: predicting isoform-disease associations by data fusion and deep neural networks

Alternative splicing produces different isoforms from the same gene locus. Although the prediction of gene(miRNA)-disease associations have been extensively studied, few (or no) computational solutions have been proposed for the prediction of isoform-disease association (IDA) at a large scale, mainly due to the lack of disease annotations of isoforms. However, increasing evidences confirm the close connections between diseases and isoforms, which can more precisely uncover the pathology of complex diseases. Therefore, it is highly desirable to predict IDAs. To bridge this gap, we propose a deep neural network based solution (DeepIDA) to fuse multi-type genomics and transcriptomics data to predict IDAs. Particularly, DeepIDA uses gene-isoform relations to dispatch gene-disease associations to isoforms. In addition, it utilizes two DNN sub-networks with different structures to capture nucleotide and expression features of isoforms, Gene Ontology data and miRNA target data, respectively. After that, these two sub-networks are merged in a dense layer to predict IDAs. The experimental results on public datasets show that DeepIDA can effectively predict IDAs with AUPRC of 0.9141 and macro F-measure of 0.9155, which are much higher than those of competitive methods. Further study on sixteen isoform-disease association cases again corroborate the superiority of DeepIDA.