DMILIII: Isoformisoform interaction prediction using deep multiinstance learning method Alternative splicing modulates proteinprotein 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 proteinprotein interaction at the isoformlevel is crucial to explore the function of proteoforms. Constructing the isoformlevel interaction network currently is prohibited by the lack of a large golden set of experimentally validated interacting isoforms, which enable computationally predicting isoformisoform interactions. In this paper, a deep convolution neural network based multiinstance learning approach called DMILIII is proposed to predict isoform interactions. DMILIII takes a gene pair as xe2x80x98bagxe2x80x99 and two isoforms of the pairwise genes as the xe2x80x98instancexe2x80x99 of the bag. DMILIII follows the principle of multiinstance learning that at least one isoformisoform interaction exists for a positive gene pair and none interacting isoforms occurs for a negative gene pair. DMILIII integrates RNAseq, nucleotide sequence, domaindomain interaction and exon array data. Experimental results indicate that DMILIII achieves a superior performance with Accuracy of 93% on singleinstance gene bags and of 94% on multiinstance gene bags, which are at least 14% and 29% higher than those of stateoftheart methods. In addition, we further test DMILIII on a set of experimentally confirmed isoformisoform interactions and obtain an Accuracy of 65%, which is at least 10% higher than those of comparing methods at the isoformlevel. All these results show the effectiveness of DMILIII for predicting isoformisoform interactions.

LncRNAmiRNA interaction prediction from the heterogeneous network through graph embedding ensemble learning LncRNAmiRNA interactions play crucial roles in gene regulatory networks and can reveal functions of lncRNAs and miRNAs. Although several methods have been proposed to infer interactions on the lncRNAmiRNA interaction network, few attentions have been paid to fully exploiting the structure of lncRNAmiRNA interaction network. In this paper, we propose a Graph Embedding Ensemble Learning method (abbreviated as xe2x80x9cGEELxe2x80x9d) to predict lncRNAmiRNA interactions. First, we collect lncRNA sequences and miRNA sequences to calculate lncRNAlncRNA sequence similarity and miRNAmiRNA sequence similarity, and then we combine them with the known lncRNAmiRNA interactions to construct a heterogeneous network, which takes lncRNAs and miRNAs as nodes. We adopt graph embedding methods to learn representations of lncRNAs and miRNAs from the heterogeneous network, and then merge the representations of lncRNAs and miRNAs to represent the lncRNAmiRNA pairs. Random forest classifiers are built based on the merged representations to predict lncRNAmiRNA interactions. We consider five different graph embedding methods, and evaluate the corresponding models. Further, we use individual graph embedding methodbased model as base predictors and build a highlevel ensemble model. The experimental results show that GEEL achieves AUPR score of 0.7004 and AUC score of 0.9537, and outperforms base predictors and other stateoftheart methods. In conclusion, GEEL is an effective tool for lncRNAmiRNA interaction prediction.