DMIL-III: Isoform-isoform interaction prediction using deep multi-instance learning method

Abstract

Alternative splicing modulates protein-protein and other ligand interactions, it results in proteoforms, translated from isoforms that are alternatively spliced from the same gene, to interact with different partners and have distinct or even opposing functions. Therefore, systematically identifying protein-protein interaction at the isoform-level is crucial to explore the function of proteoforms. Constructing the isoform-level interaction network currently is prohibited by the lack of a large golden set of experimentally validated interacting isoforms, which enable computationally predicting isoform-isoform interactions. In this paper, a deep convolution neural network based multi-instance learning approach called DMIL-III is proposed to predict isoform interactions. DMIL-III takes a gene pair as `bag' and two isoforms of the pairwise genes as the `instance' of the bag. DMIL-III follows the principle of multi-instance learning that at least one isoform-isoform interaction exists for a positive gene pair and none interacting isoforms occurs for a negative gene pair. DMIL-III integrates RNA-seq, nucleotide sequence, domain-domain interaction and exon array data. Experimental results indicate that DMIL-III achieves a superior performance with Accuracy of 93% on single-instance gene bags and of 94% on multi-instance gene bags, which are at least 14% and 29% higher than those of state-of-the-art methods. In addition, we further test DMIL-III on a set of experimentally confirmed isoform-isoform interactions and obtain an Accuracy of 65%, which is at least 10% higher than those of comparing methods at the isoform-level. All these results show the effectiveness of DMIL-III for predicting isoform-isoform interactions.