ArticleCells2019
Convolutional Neural Network and Bidirectional Long Short-Term Memory-Based Method for Predicting Drug-Disease Associations.
Article in Cells, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- A Graph Neural Network Framework with Equivariance and Heterogeneity for Drug Repositioning.Interdisciplinary sciences, computational life sciences · 2026Article
- MetaAMPK: Accurate Prediction of Adenosine Monophosphate-Activated Protein Kinase Activators Using a Meta-Learner Neural Network.ACS omega · 2025Article
- A comprehensive landscape of AI applications in broad-spectrum drug interaction prediction: a systematic review.Journal of cheminformatics · 2025Review
- Dual-route embedding-aware graph neural networks for drug repositioning.Briefings in bioinformatics · 2025Article
- Reconciling multiple connectivity-based systems biology methods for drug repurposing.Briefings in bioinformatics · 2025Review
- Machine learning-assisted analysis of serum metabolomics and network pharmacology reveals the effective compound from herbal formula against alcoholic liver injury.Chinese medicine · 2025Article
- Leveraging Network Target Theory for Efficient Prediction of Drug-Disease Interactions: A Transfer Learning Approach.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Drug repositioning based on residual attention network and free multiscale adversarial training.BMC bioinformatics · 2024Article
- CBIL-VHPLI: a model for predicting viral-host protein-lncRNA interactions based on machine learning and transfer learning.Scientific reports · 2024Article
- Explainable drug repurposing via path based knowledge graph completion.Scientific reports · 2024Article
- Biomedical knowledge graph learning for drug repurposing by extending guilt-by-association to multiple layers.Nature communications · 2023Article
- ITRPCA: a new model for computational drug repositioning based on improved tensor robust principal component analysis.Frontiers in genetics · 2023Article
- Integration of Neighbor Topologies Based on Meta-Paths and Node Attributes for Predicting Drug-Related Diseases.International journal of molecular sciences · 2022Article
- Recognition of pulmonary diseases from lung sounds using convolutional neural networks and long short-term memory.Journal of ambient intelligence and humanized computing · 2022Article
- Article
- CNNDLP: A Method Based on Convolutional Autoencoder and Convolutional Neural Network with Adjacent Edge Attention for Predicting lncRNA-Disease Associations.International journal of molecular sciences · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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