ArticleBMC bioinformatics2019
Additional Neural Matrix Factorization model for computational drug repositioning.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 39 citations in OpenAlex.
- Early prediction and risk assessment of adverse drug combinations using ensemble learning.Scientific reports · 2026Article
- Integrating fuzzy logic and neural networks with multi-criteria decision-making for intelligent evaluation of drug compound design attributes.Scientific reports · 2026Article
- Uncertainty-aware hybrid deep generative framework for robust and explainable drug discovery.BMC bioinformatics · 2026Article
- Strategies for robust, accurate, and generalizable benchmarking of drug discovery platforms.Bioinformatics (Oxford, England) · 2025Article
- Strategies for robust, accurate, and generalizable benchmarking of drug discovery platforms.bioRxiv : the preprint server for biology · 2025Article
- FACT: Feature Aggregation and Convolution with Transformers for predicting drug classification code.Bioinformatics (Oxford, England) · 2025Article
- Identifying behavior regulatory leverage over mental disorders transcriptomic network hubs toward lifestyle-dependent psychiatric drugs repurposing.Human genomics · 2025Article
- Comprehensive evaluation of pure and hybrid collaborative filtering in drug repurposing.Scientific reports · 2025Article
- Computational drug repositioning with attention walking.Scientific reports · 2024Article
- A comparative benchmarking and evaluation framework for heterogeneous network-based drug repositioning methods.Briefings in bioinformatics · 2024Article
- Prediction of drug-disease associations based on reinforcement symmetric metric learning and graph convolution network.Frontiers in pharmacology · 2024Article
- Predicting drug-target interactions using matrix factorization with self-paced learning and dual similarity information.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Article
- Predicting Drug-Gene-Disease Associations by Tensor Decomposition for Network-Based Computational Drug Repositioning.Biomedicines · 2023Article
- Drug-Disease Association Prediction Using Heterogeneous Networks for Computational Drug Repositioning.Biomolecules · 2022Review
- Hybrid attentional memory network for computational drug repositioning.BMC bioinformatics · 2020Article
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundComputational drug repositioning, which aims to find new applications for existing drugs, is gaining more attention from the pharmaceutical companies due to its low attrition rate, reduced cost, and shorter timelines for novel drug discovery. Nowadays, a growing number of researchers are utilizing the concept of recommendation systems to answer the question of drug repositioning. Nevertheless, there still lie some challenges to be addressed: 1) Learning ability deficiencies; the adopted model cannot learn a higher level of drug-disease associations from the data. 2) Data sparseness limits the generalization ability of the model. 3)Model is easy to overfit if the effect of negative samples is not taken into consideration.
resultsIn this study, we propose a novel method for computational drug repositioning, Additional Neural Matrix Factorization (ANMF). The ANMF model makes use of drug-drug similarities and disease-disease similarities to enhance the representation information of drugs and diseases in order to overcome the matter of data sparsity. By means of a variant version of the autoencoder, we were able to uncover the hidden features of both drugs and diseases. The extracted hidden features will then participate in a collaborative filtering process by incorporating the Generalized Matrix Factorization (GMF) method, which will ultimately give birth to a model with a stronger learning ability. Finally, negative sampling techniques are employed to strengthen the training set in order to minimize the likelihood of model overfitting. The experimental results on the Gottlieb and Cdataset datasets show that the performance of the ANMF model outperforms state-of-the-art methods.
conclusionsThrough performance on two real-world datasets, we believe that the proposed model will certainly play a role in answering to the major challenge in drug repositioning, which lies in predicting and choosing new therapeutic indications to prospectively test for a drug of interest.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.