Convolutional Neural Network and Bidirectional Long Short-Term Memory-Based Method for Predicting Drug-Disease Associations.

Xuan Xuan,Zhang Zhang,Sun Sun,Zhao Zhao,Ye Ye

doi:10.3390/cells8070705

Xuan Xuan, Zhang Zhang + Show 3 more

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https://doi.org/10.3390/cells8070705

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Abstract

Identifying novel indications for approved drugs can accelerate drug development and reduce research costs. Most previous studies used shallow models for prioritizing the potential drug-related diseases and failed to deeply integrate the paths between drugs and diseases which may contain additional association information. A deep-learning-based method for predicting drug–disease associations by integrating useful information is needed. We proposed a novel method based on a convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM)—CBPred—for predicting drug-related diseases. Our method deeply integrates similarities and associations between drugs and diseases, and paths among drug-disease pairs. The CNN-based framework focuses on learning the original representation of a drug-disease pair from their similarities and associations. As the drug-disease association possibility also depends on the multiple paths between them, the BiLSTM-based framework mainly learns the path representation of the drug-disease pair. In addition, considering that different paths have discriminate contributions to the association prediction, an attention mechanism at path level is constructed. Our method, CBPred, showed better performance and retrieved more real associations in the front of the results, which is more important for biologists. Case studies further confirmed that CBPred can discover potential drug-disease associations.

Highlights

The research and development (R&D) stage of producing a novel drug is a time-consuming, complex, and costly process that normally lasts for more than ten years and costs approximately1 billion dollars [1,2,3,4]
We present CBPred, a novel method for predicting the potential drug–disease associations
To comprehensively consider original information and topological information of the drug–disease pair, we designed a novel prediction model based on the convolutional neural network (CNN) module and bidirectional long short-term memory (BiLSTM) module

Summary

Introduction

The research and development (R&D) stage of producing a novel drug is a time-consuming, complex, and costly process that normally lasts for more than ten years and costs approximately1 billion dollars [1,2,3,4]. Because approved drugs have undergone the necessary clinical trials, their safety has been evaluated, identifying new indications for these drugs, (i.e., drug repositioning), which can effectively reduce the time and costs for drug-related R&D [5,8,9]. Computational prediction of the associations between drugs and diseases can identify candidates for further wet-lab validation [12,13]. Several methods are used to predict and prioritize drug-associated diseases, which can generally be divided into two categories. Methods in the first category capture network topology information using a diffusion algorithm and provide association scores for candidate diseases [14,15,16,17]. Wang et al [16] identified candidate diseases using an iterative update

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Conclusion