A Hybrid Model Integrating Multi-Omic and Topological Information of PPI Network for Drug Synergism Prediction
Giang T.T. Nguyen, Kien T. Phuong, Khanh Nguyen-Trong, Duc‐Hau Le · 2023
Combination therapies in precision medicine promise to enhance treatment efficacy and combat drug resistance. Current drug combination prediction methods struggle to extract valuable insights from multi-omics cell line data and integrate them with protein-protein interaction (PPI) network information. To address these challenges, we introduce AE-XGBSynergy, a novel approach. AE-XGBSynergy integrates multi-omics cell line data and drug-cell line features derived from the PPI network's topological characteristics to predict drug synergy combinations. It employs the struc2vec algorithm to extract features for drugs and cell lines based on the PPI network's topological characteristics. Additionally, a pre-trained encoder captures abstract representations of high-dimensional cell line data. These representations of drug pairs and cell lines serve as input for an extreme gradient-boosting algorithm to predict drug combinations for cancer cell lines. We assessed our model's performance using two datasets: the O'Neil and DrugCombDB datasets, which provide extensive drug combination information. Our experimental results show that AE-XGBSynergy excels at integrating multi-omics data to predict drug synergies, with genomic data playing a particularly significant role. Notably, when compared to NEXGB, a hybrid model that includes drug and cell line features using a PPI network, our approach outperforms in terms of accuracy (ACC), AVC-ROC, AUC-PR, Precision, and F1-score.