Layer-stacked Attention for Heterogeneous Network Embedding

The heterogeneous network is a robust data abstraction that can model entities of different types interacting in various ways. Such heterogeneity brings rich semantic information but presents nontrivial challenges in aggregating the heterogeneous relationships between objects - especially those of higher-order indirect relations. Recent graph neural network approaches for representation learning on heterogeneous networks typically employ the attention mechanism, which is often only optimized for predictions based on direct links. Furthermore, even though most deep learning methods can aggregate higher-order information by building deeper models, such a scheme can diminish the degree of interpretability. To overcome these challenges, we explore an architecture - Layer-stacked ATTention Embedding (LATTE) - that automatically decomposes higher-order meta relations at each layer to extract the relevant heterogeneous neighborhood structures for each node. Additionally, by successively stacking layer representations, the learned node embedding offers a more interpretable aggregation scheme for nodes of different types at different neighborhood ranges. We conducted experiments on several benchmark heterogeneous network datasets. In both transductive and inductive node classification tasks, LATTE can achieve state-of-the-art performance compared to existing approaches, all while offering a lightweight model. With extensive experimental analyses and visualizations, the framework can demonstrate the ability to extract informative insights on heterogeneous networks.

Functional Representation of Large-Scale Heterogeneous RNA Sequences with Integration of Diverse Multi-omics, Interactions, and Annotations Data

Long non-coding RNA, microRNA, and messenger RNA enable key regulations of various biological processes through a variety of diverse interaction mechanisms. Identifying the interactions and cross-talk between these heterogeneous RNA classes is essential in order to uncover the functional role of individual RNA transcripts, especially for unannotated and newly-discovered RNA sequences with no known interactions. Recently, sequence-based deep learning and network embedding methods are becoming promising approaches that can either predict RNA-RNA interactions from a sequence or infer missing interactions from patterns that may exist in the network topology. However, the majority of these methods have several limitations, eg, the inability to perform inductive predictions, to distinguish the directionality of interactions, or to integrate various sequence, interaction, and annotation biological datasets. We proposed a novel deep learning-based framework, rna2rna, which learns from RNA sequences to produce a low-dimensional embedding that preserves the proximities in both the interactions topology and the functional affinity topology. In this proposed embedding space, we have designated a two-part" source and target contexts" to capture the targeting and receptive fields of each RNA transcript, while encapsulating the heterogenous cross-talk interactions between lncRNAs and miRNAs. From experimental results, our method exhibits superior performance in AUPR rates compared to state-of-art approaches at predicting missing interactions in different RNA-RNA interaction databases and was shown to accurately perform link predictions to novel RNA sequences not seen at training time, even without any prior information. Additional results suggest that our proposed framework can capture a manifold for heterogeneous RNA sequences to discover novel functional annotations.