ArticleBMC bioinformatics2018
Predicting drug-disease associations by using similarity constrained matrix factorization.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 111 papers.
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111 citing papers in PubMed.
- Subgraph Neural Networks Enhanced by Global Similarity for Drug Repositioning.Interdisciplinary sciences, computational life sciences · 2026Article
- KGTC-DDA:Drug-disease association prediction based on parallel graph isomorphism and transformer networks with cross-attention.Biochemistry and biophysics reports · 2026Article
- Spatial-spectral fusion enables drug repositioning by capturing indirect and long-range associations in biological networks.Bioinformatics (Oxford, England) · 2026Article
- LEARNER: A Transfer Learning Method for Low-Rank Matrix Estimation.Statistics in medicine · 2026Article
- Reviewing the Computational Landscape of Drug Repurposing: Evolution from Structure-Based Methods to LLM-Based Methods.Biomolecules · 2026Review
- HNRM: hyperedge neighborhood-based representation for predicting N6-methyladenosine-related regulatory pathways.BMC biology · 2026Article
- DMAPLM: A multimodal pretrained framework for computational drug repositioning.PLoS computational biology · 2026Article
- MMADPE: drug repositioning based on multi-hop graph Mamba aggregation with dual-modality graph positional encoding.Molecular diversity · 2026Article
- From General-Purpose to Disease-Specific Features: Aligning LLM Embeddings on a Disease-Specific Biomedical Knowledge Graph for Drug Repurposing.bioRxiv : the preprint server for biology · 2026Article
- FKSUDDAPre: A drug-disease association prediction framework based on F-TEST feature selection and AMDKSU resampling with interpretability analysis.PLoS computational biology · 2026Article
- Multi-DDA: drug-disease association prediction using a hybrid graph convolutional network with multi-modal drug representations.Bioinformatics advances · 2026Article
- THGC_MDA: a method for predicting the associations between mFrontiers in genetics · 2026Article
- Node2Vec-DGI-EL: a hierarchical graph representation learning model for ingredient-disease association prediction.Bioinformatics advances · 2026Article
- Benchmarking heterogeneous network-based methods for drug repurposing.NPJ systems biology and applications · 2025Article
- Computational prediction and conformation of relationships among microbes, drugs and diseases.Scientific reports · 2025Article
- Strategies for robust, accurate, and generalizable benchmarking of drug discovery platforms.Bioinformatics (Oxford, England) · 2025Article
- Large Language Model-Enhanced Drug Repositioning Knowledge Extraction via Long Chain-of-Thought: Development and Evaluation Study.JMIR medical informatics · 2025Article
- Drug-disease networks and drug repurposing.PLoS computational biology · 2025Article
- A comprehensive landscape of AI applications in broad-spectrum drug interaction prediction: a systematic review.Journal of cheminformatics · 2025Review
- Multi-Modal Topology-Aware Graph Neural Network for Robust Chemical-Protein Interaction Prediction.International journal of molecular sciences · 2025Article
51 more citing papers are in PubMed but not listed here.
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7 authors.
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Abstract
backgroundDrug-disease associations provide important information for the drug discovery. Wet experiments that identify drug-disease associations are time-consuming and expensive. However, many drug-disease associations are still unobserved or unknown. The development of computational methods for predicting unobserved drug-disease associations is an important and urgent task.
resultsIn this paper, we proposed a similarity constrained matrix factorization method for the drug-disease association prediction (SCMFDD), which makes use of known drug-disease associations, drug features and disease semantic information. SCMFDD projects the drug-disease association relationship into two low-rank spaces, which uncover latent features for drugs and diseases, and then introduces drug feature-based similarities and disease semantic similarity as constraints for drugs and diseases in low-rank spaces. Different from the classic matrix factorization technique, SCMFDD takes the biological context of the problem into account. In computational experiments, the proposed method can produce high-accuracy performances on benchmark datasets, and outperform existing state-of-the-art prediction methods when evaluated by five-fold cross validation and independent testing.
conclusionWe developed a user-friendly web server by using known associations collected from the CTD database, available at http://www.bioinfotech.cn/SCMFDD/ . The case studies show that the server can find out novel associations, which are not included in the CTD database.
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