Adaptive-weighted federated graph convolutional networks with multi-sensor data fusion for drug response prediction

Abstract

Drug response prediction is a vital task owing to the heterogeneity of cancer patients, enabling individualized therapy. Graph convolutional networks (GCNs) are able to predict the response of cancer cell lines and patients to novel drugs or drug combinations. Furthermore, GCNs leveraging multi-sensor data can improve drug response prediction accuracy. However, it is a challenge for GCNs to build an efficient method to enable data sharing between different institutions because of data privacy and security. This would not integrate multi-sensor data, leading to a decrease of the data scale, which decreases model performance. Besides, heterogeneous noises exist in multi-sensor data, which decreases the performance of the learning system. To this end, we propose a novel adaptive-weighted federated graph convolutional networks (called AFGCNs) based on heterogeneous multisource multiomics-drug data privacy-preserving fusion to predict drug response. Specifically, AFGCNs can integrate various multiomics and drug data to learn key internal relations under privacy protection. Meanwhile, AFGCNs can capture association relationships between multisource data in multiple parties to reweight these multisource data for denoising, which can improve the performance of AFGCNs. The experimental results have demonstrated that AFGCNs outperforms state-of-the-art comparison methods by a large margin for drug response prediction, including single drugs as well as targeted drugs. More specifically, the AFGCNs exceeds the average value of all comparison methods by approximately 8% in terms of F1 Score metric in the random cross validation experiment. Furthermore, molecular docking experiments are conducted to validate the model’s performance in accurately predicting the target drug. In general, AFGCNs is regarded as a successful method for bridging the gap between multiple institutions and maintaining data security and privacy, which provides an effective way to accelerate drug discovery.