Abstract
MOTIVATION: Protein-protein interactions (PPIs) play essential roles in the buildup of cellular machinery and provide the skeleton for cellular signaling. However, these biochemical roles are context dependent and interactions may change across cell type, time, and space. In contrast, PPI detection assays are run in a single condition that may not even be an endogenous condition of the organism, resulting in static networks that do not reflect full cellular complexity. Thus, there is a need for computational methods to predict cell-type-specific interactions. RESULTS: Here we present SPIDER (Supervised Protein Interaction DEtectoR), a graph attention-based model for predicting cell-type-specific PPI networks. In contrast to previous attempts at this problem, which were unsupervised in nature, our model's training is guided by experimentally measured cell-type-specific networks, enhancing its performance. We evaluate our method using experimental data of cell-type-specific networks from both humans and mice, and show that it outperforms current approaches by a large margin. We further demonstrate the ability of our method to generalize the predictions to datasets of tissues lacking prior PPI experimental data. We leverage the networks predicted by the model to facilitate the identification of tissue-specific disease genes. AVAILABILITY AND IMPLEMENTATION: Our code and data are available at https://github.com/Kuper994/SPIDER.