Drug-target interactions prediction based on network topology feature representation embedded deep forest

Identifying drug-target interactions (DTIs) is instructive in drug design and disease treatment. Existing studies typically used the properties of nodes (drug chemical structure and protein sequence) to construct drug and target features while ignoring the influence of network topology information on the prediction of DTIs. In this study, a hybrid computation model is proposed to predict DTIs based on the network topological feature representation embedded the deep forest model (NTFRDF). The main idea is to capture the topological differences by learning the low-dimensional feature representation of drugs and targets from the heterogeneous network. In addition, the multi-similarity fusion strategy is proposed to mine hidden useful information in the known DTIs from multi-view to enrich network features of the heterogeneous network. Based on the deep forest framework, the performance of the proposed method is examined on four benchmark datasets. Our experimental results verify that the proposed method is competitive compared with some existing DTIs prediction models.