Tensor Factorisation for Polypharmacy Side Effect Prediction
Oliver Lloyd Yi Liu Tom R. Gaunt
Adverse reactions caused by drug combinations are an increasingly common
phenomenon, making their accurate prediction an important challenge in modern
medicine. However, the polynomial nature of this problem renders lab-based
identification of adverse reactions insufficient. Dozens of computational
approaches have therefore been proposed for the task in recent years, with
varying degrees of success. One group of methods that has seemingly been
under-utilised in this area is tensor factorisation, despite their clear
applicability to this type of data. In this work, we apply three such models to
a benchmark dataset in order to compare them against established techniques. We
find, in contrast to previous reports, that for this task tensor factorisation
models are competitive with state-of-the-art graph neural network models and we
recommend that future work in this field considers cheaper methods with linear
complexity before running costly deep learning processes.