Hybrid Attentional Memory Network for Computational drug repositioning

TL;DR

The paper introduces HAMN, a hybrid deep learning model combining neighborhood-based and latent factor collaborative filtering for drug repositioning, effectively addressing cold start issues and improving prediction accuracy.

Contribution

This work is the first to integrate neighborhood and latent factor CF models with attention and memory mechanisms for drug repositioning.

Findings

HAMN outperforms existing models on real datasets.

The model effectively alleviates cold start problems.

Experimental results show superior AUC, AUPR, and HR metrics.

Abstract

Drug repositioning is designed to discover new uses of known drugs, which is an important and efficient method of drug discovery. Researchers only use one certain type of Collaborative Filtering (CF) models for drug repositioning currently, like the neighborhood based approaches which are good at mining the local information contained in few strong drug-disease associations, or the latent factor based models which are effectively capture the global information shared by a majority of drug-disease associations. Few researchers have combined these two types of CF models to derive a hybrid model with the advantages of both of them. Besides, the cold start problem has always been a major challenge in the field of computational drug repositioning, which restricts the inference ability of relevant models. Inspired by the memory network, we propose the Hybrid Attentional Memory Network (HAMN) model, a deep architecture combines two classes of CF model in a nonlinear manner. Firstly, the memory unit and the attention mechanism are combined to generate the neighborhood contribution representation to capture the local structure of few strong drug-disease associations. Then a variant version of the autoencoder is used to extract the latent factor of drugs and diseases to capture the overall information shared by a majority of drug-disease associations. In that process, ancillary information of drugs and diseases can help to alleviate the cold start problem. Finally, in the prediction stage, the neighborhood contribution representation is combined with the drug latent factor and disease latent factor to produce the predicted value. Comprehensive experimental results on two real data sets show that our proposed HAMN model is superior to other comparison models according to the AUC, AUPR and HR indicators.