Using existing knowledge to carry out drug-disease associations prediction is a vital method for drug repositioning. However, effectively fusing the biomedical text and biological network information is one of the great challenges for most current drug repositioning methods. In this study, we propose a drug repositioning method based on heterogeneous networks and text mining (HeTDR). This model can combine drug features from multiple drug-related networks, disease features from biomedical corpora with the known drug-disease associations network to predict the correlation scores between drug and disease. Experiments demonstrate that HeTDR has excellent performance that is superior to that of state-of-the-art models. We present the top 10 novel HeTDR-predicted approved drugs for five diseases and prove our model is capable of discovering potential candidate drugs for disease indications.
We developed a novel DL-based method for drug repositioning (HeTDR)
HeTDR succeeds in fusing networks topology information and text mining information
HeTDR obtains high accuracy, excessing most state-of-the-art models
HeTDR could represent an algorithm integrating multiple sources of information
Traditional drug discovery and development are often time consuming and high risk. Drug repositioning aims to expand existing indications or discover new targets by studying the approved drug compounds, thereby reducing the time, costs, and risk of drug development. We propose a novel method in drug repositioning based on heterogeneous networks and text mining (HeTDR), which combines drugs features from multiple networks and diseases features from biomedical corpora to predict the correlation scores between drugs and diseases. This prediction model has provided a potential solution for multiple information fusion and to exhibit accurate performance leading to the discovery of new drugs for indications. This algorithm could contribute a new idea to the acceleration and development of future drug repositioning by using computational methods and provide computer-aided guidance for biologists in clinical settings.
Drug repositioning is a useful way to discover new drug candidates for curing diseases. However, integrating multiple networks and text mining information for drug repositioning is still a major challenge. We propose a drug repositioning method based on heterogeneous networks and text mining (HeTDR), which can combine drug features from multiple networks and disease features from biomedical corpora for drug repositioning. HeTDR obtains high accuracy in predicting drug-disease interactions and is capable of finding novel indications of approved drugs.