MGANSL: multi-network representation generating with generative adversarial network for synthetic lethality prediction.

backgroundCancer is a complex disease that arises from the simultaneous mutations of multiple biological molecules. An effective therapeutic strategy is to exploit synthetic lethality (SL) by targeting the SL partner of cancer driver genes. Computational approaches have emerged as efficient complements to traditional methods. Although some methods integrate heterogeneous sources to learn multi-network representations, they often neglect consistent information shared across different networks and specific characteristic specific to individual network. Therefore, a comprehensive representation learning framework for capturing both multi-network consistency and network-specific information of gene pair is needed.

resultsWe proposed a novel approach capturing Multi-network consistent and specific representation with Generative Adversarial Network for Synthetic Lethality prediction (MGANSL). MGANSL employs network-aligned and network-specific encoding modules to cooperatively learn comprehensive multi-network representations of gene pair. In particular, network-aligned encoding module can capture cross-modal consistent information via cross-network adversarial generation, and network-specific encoding module can capture single network specific information via intra-network adversarial generation.

conclusionsComprehensive experiments conducted on two human synthetic lethality datasets demonstrate the superiority of proposed method in SL prediction. Moreover, the novel predicted SL associations could aid in designing anti-cancer drugs and providing potential drug targets.