Affiliation:
1. College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, 410082, China
2. Geneis Beijing Co., Ltd, Beijing, 100102, China
Abstract
Abstract
Drug repositioning is an efficient and promising strategy for traditional drug discovery and development. Many research efforts are focused on utilizing deep-learning approaches based on a heterogeneous network for modeling complex drug–disease associations. Similar to traditional latent factor models, which directly factorize drug–disease associations, they assume the neighbors are independent of each other in the network and thus tend to be ineffective to capture localized information. In this study, we propose a novel neighborhood and neighborhood interaction-based neural collaborative filtering approach (called DRWBNCF) to infer novel potential drugs for diseases. Specifically, we first construct three networks, including the known drug–disease association network, the drug–drug similarity and disease–disease similarity networks (using the nearest neighbors). To take the advantage of localized information in the three networks, we then design an integration component by proposing a new weighted bilinear graph convolution operation to integrate the information of the known drug–disease association, the drug’s and disease’s neighborhood and neighborhood interactions into a unified representation. Lastly, we introduce a prediction component, which utilizes the multi-layer perceptron optimized by the α-balanced focal loss function and graph regularization to model the complex drug–disease associations. Benchmarking comparisons on three datasets verified the effectiveness of DRWBNCF for drug repositioning. Importantly, the unknown drug–disease associations predicted by DRWBNCF were validated against clinical trials and three authoritative databases and we listed several new DRWBNCF-predicted potential drugs for breast cancer (e.g. valrubicin and teniposide) and small cell lung cancer (e.g. valrubicin and cytarabine).
Funder
Natural Science Foundation of Hunan Province
Changsha Key Research and Development Program
National Natural Science Foundation of China
Publisher
Oxford University Press (OUP)
Subject
Molecular Biology,Information Systems
Cited by
65 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献