HNet-DNN: Inferring New Drug–Disease Associations with Deep Neural Network Based on Heterogeneous Network Features

Abstract

Drug research and development is a time-consuming and high-cost task, pressing an urgent demand to identify novel indications of approved drugs, referred to as drug repositioning, which provides an economical and efficient way for drug discovery. With increasing volumes of large-scale chemical, genomic, and pharmacological data sets generated by the high-throughput technique, it is crucial to develop systematic and rational computational approaches to identify new indications of approved drugs. In this paper, we introduce HNet-DNN, which utilizes a deep neural network (DNN), to predict new drug–disease associations based on the features extracted from the drug–disease heterogeneous network. Instead of the straightforward concatenation of chemical and phenotypic features as the input of DNN, we used these raw features of drugs and diseases to construct a drug–drug similarity network and a disease–disease similarity network, and then built a drug–disease heterogeneous network by integrating known drug–disease associations. Subsequently, we extracted topological features for drug–disease associations from the heterogeneous network and used them to train a DNN model. Our intensive performance evaluations demonstrated that HNet-DNN effectively exploits the features of the heterogeneous network to boost the predictive performance of drug–disease associations. Compared with a couple of typical classifiers and competitive approaches, our method not only achieved state-of-the-art performance but also effectively alleviated the overfitting problem. Moreover, we ran HNet-DNN to predict new drug–disease associations and carried out case studies to verify the effectiveness of our method.

HeTDR: Drug repositioning based on heterogeneous networks and text mining

“…We compare our results with seven methods: SVM, 29 Katz, 30 MBiRW, 14 DTINet, 31 DRRS, 32 DeepDR, 10 and HNet-DNN. 12 SVM is the traditional machine learning algorithm; Katz is the path-based classic algorithm calculating similarities between nodes in a network for associations prediction; MBiRW utilizes some comprehensive similarity measures and the BiRandom Walk (BiRW) algorithm for drug repositioning; DTINet is a matrix factorization-based model and it can integrate diverse information from heterogeneous networks; DRRS is a computational drug repositioning method using low-rank matrix appropriation and randomized algorithm; DeepDR is a network-based deep learning method to infer potential novel drug-disease associations; HNet-DNN uses the deep neural network (DNN) to predict new drug-disease interactions. We use AUROC and AUPR to evaluate the performance of these methods.…”

“…We compare our results with seven methods: SVM, 29 Katz, 30 MBiRW, 14 DTINet, 31 DRRS, 32 DeepDR, 10 and HNet-DNN. 12…”

“…The network-based methods are the most widely used methods for drug repositioning. Its mainstream algorithms include: network inference, [9][10][11][12] random walk, 13,14 and matrix factorization. 15,16 These methods usually rely heavily on the richness of interaction network data.…”

Hybrid Deep Learning Based on a Heterogeneous Network Profile for Functional Annotations of Plasmodium falciparum Genes

“…This knowledge potentially helps to reveal novel therapeutic approaches to treating malaria. A heterogeneous network—that is, a network connecting two or more different types of networks—has been applied in several studies [ 16 , 21 , 22 ]. Suratanee et al [ 16 ] used a network propagation algorithm on a heterogeneous network to find associations between Plasmodium vivax and human proteins.…”

“…To draw information from the network, topological features could be extracted. Liu et al [ 21 ] extracted topological features from a heterogeneous network of drugs and diseases and used a deep neural network (DNN) to predict new drug–disease associations. A deep neural network was also used to predict protein–protein interactions from common protein descriptors [ 23 ].…”