Isoform-Disease Association Prediction by Data Fusion

Abstract

Alternative splicing enables a gene spliced into different isoforms, which are closely related with diverse developmental abnormalities. Identifying the isoform-disease associations helps to uncover the underlying pathology of various complex diseases, and to develop precise treatments and drugs for these diseases. Although many approaches have been proposed for predicting gene-disease associations and isoform functions, few efforts have been made toward predicting isoform-disease associations in large-scale, the main bottleneck is the lack of ground-truth isoform-disease associations. To bridge this gap, we propose a multi-instance learning inspired computational approach called IDAPred to fuse genomics and transcriptomics data for isoform-disease association prediction. Given the bag-instance relationship between gene and its spliced isoforms, IDAPred introduces a dispatch and aggregation term to dispatch gene-disease associations to individual isoforms, and reversely aggregate these dispatched associations to affiliated genes. Next, it fuses different genomics and transcriptomics data to replenish gene-disease associations and to induce a linear classifier for predicting isoform-disease associations in a coherent way. In addition, to alleviate the bias toward observed gene-disease associations, it adds a regularization term to differentiate the currently observed associations from the unobserved (potential) ones. Experimental results show that IDAPred significantly outperforms the related state-of-the-art methods.