Cancer is a multifaceted disease that results from co-mutations of multi biological molecules. A promising strategy for cancer therapy involves in exploiting the phenomenon of Synthetic Lethality (SL) by targeting the SL partner of cancer gene. Since traditional methods for SL prediction suffer from high-cost, time-consuming and off-targets effects, computational approaches have been efficient complementary to these methods. Most of existing approaches treat SL associations as independent of other biological interaction networks, and fail to consider other information from various biological networks. Despite some approaches have integrated different networks to capture multi-modal features of genes for SL prediction, these methods implicitly assume that all sources and levels of information contribute equally to the SL associations. As such, a comprehensive and flexible framework for learning gene cross-network representations for SL prediction is still lacking. In this work, we present a novel Triple-Attention cross-network Representation learning for SL prediction (TARSL) by capturing molecular features from heterogeneous sources. We employ three-level attention modules to consider the different contribution of multi-level information. In particular, feature-level attention can capture the correlations between molecular feature and network link, node-level attention can differentiate the importance of various neighbors, and network-level attention can concentrate on important network and reduce the effects of irrelated networks. We perform comprehensive experiments on human SL datasets and these results have proven that our model is consistently superior to baseline methods and predicted SL associations could aid in designing anti-cancer drugs.

Computational methods to discover potential synthetic lethality (SL) pairs has become a promising strategy for targeted cancer therapy and cancer medicine development. Despite many computational methods by integrating multiple biological networks were proposed to improve the identification performance. It is essential to propose feature representation approach via embedding latent biological variables in various networks into a unified feature space. Therefore, we propose a method to identify synthetic lethality genes by modeling latent space with embedding variables resulting from the potential interpretation of synthetic lethality on integrating heterogeneous networks (LSTF) to obtain gene representation. Meanwhile, manifold subspace regularization is applied to capture the geometrical manifold structure in the latent space with gene PPI functional and GO semantic embeddings. Subsequently, SL gene pairs are identified by the reconstruction of the associations with gene representations in the latent space. The comprehensive experimental results illustrate that LSTF is superior to other state-of-the-art methods. Case study demonstrates the effectiveness of the identified potential SL genes.