tx9b00238_si_001.zip (2.63 MB)
Prediction of Adverse Drug Reactions by Combining Biomedical Tripartite Network and Graph Representation Model
dataset
posted on 2019-12-13, 15:53 authored by Rui Xue, Jie Liao, Xin Shao, Ke Han, Jingbo Long, Li Shao, Ni Ai, Xiaohui FanAs one of the primary contributors to high clinical attrition
rates
of drugs, toxicity evaluation is of critical significance to new drug
discovery. Unsurprisingly, a vast number of computational methods
have been developed at various stages of development pipeline to evaluate
potential adverse drug reactions (ADRs). Despite previous success
of these methods on individual ADR or certain drug family, there are
great challenges to toxicity evaluation. In this study, a novel strategy
was developed to predict the drug–ADR associations by combining
deep learning and the biomedical tripartite network. This heterogeneous
network contains biomedical linked data of three entities, for example,
drugs, targets, and ADRs. For the first time, GraRep, a deep learning
method for distributed representations, is introduced to learn graph
representations and identify hidden features from the tripartite network
which are further used for ADR prediction. Through this approach,
drug–ADR associations could possibly be discovered from a systemic
perspective. The accuracy of our method is 0.95 based on internal
resource validation and 0.88 based on external resource validation.
Moreover, our results show the prediction accuracy using the tripartite
network is better than the one with bipartite network, suggesting
the model performance can be improved with further enrichment on information.
According to the result of 10-fold cross validation, the deep learning
model outperforms two traditional methods (topology-based measures
and chemical structure-based measures). Additionally,
predictive models are also constructed using other deep learning methods,
and comparable results are achieved. In summary, the biomedical tripartite
network-based deep learning model proposed here proves to offer a
promising solution for prediction of ADRs.