Latent space feature representation on multiple biological network for synthetic lethality interaction prediction

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.