ArticleIET systems biology2020
Efficient prediction of drug-drug interaction using deep learning models.
Article in IET systems biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
32 citing papers in PubMed.
- DTBA-net: Drug-Target Binding Affinity prediction using feature selection in hybrid CNN model.Journal of computer-aided molecular design · 2025Article
- Trends in drug-drug interactions for new drug clinical trials in China over the past 10 years (2013-2022).BMC pharmacology & toxicology · 2025Article
- A novel deep sequential learning architecture for drug drug interaction prediction using DDINet.Scientific reports · 2025Article
- DTI-MHAPR: optimized drug-target interaction prediction via PCA-enhanced features and heterogeneous graph attention networks.BMC bioinformatics · 2025Article
- Accurate prediction of drug combination risk levels based on relational graph convolutional network and multi-head attention.Journal of translational medicine · 2024Article
- DDI-Transform: A neural network for predicting drug-drug interaction events.Quantitative biology (Beijing, China) · 2024Article
- Artificial Intelligence in Pediatrics: Learning to Walk Together.Turkish archives of pediatrics · 2024Article
- Synergistic D-Amino Acids Based Antimicrobial Cocktails Formulated via High-Throughput Screening and Machine Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Multimodal CNN-DDI: using multimodal CNN for drug to drug interaction associated events.Scientific reports · 2024Article
- DiCleave: a deep learning model for predicting human Dicer cleavage sites.BMC bioinformatics · 2024Article
- Predict lncRNA-drug associations based on graph neural network.Frontiers in genetics · 2024Article
- PRID: Prediction Model Using RWR for Interactions between Drugs.Pharmaceutics · 2023Article
- Machine learning in metastatic cancer research: Potentials, possibilities, and prospects.Computational and structural biotechnology journal · 2023Review
- Deep Transfer Learning Based Classification Model for COVID-19 Disease.Ingenierie et recherche biomedicale : IRBM = Biomedical engineering and research · 2022Article
- Two-Way Feature Extraction for Speech Emotion Recognition Using Deep Learning.Sensors (Basel, Switzerland) · 2022Article
- Representation of molecules for drug response prediction.Briefings in bioinformatics · 2022Review
- Deep Ensemble Learning-Based Models for Diagnosis of COVID-19 from Chest CT Images.Healthcare (Basel, Switzerland) · 2022Article
- Wearable Sensors with Internet of Things (IoT) and Vocabulary-Based Acoustic Signal Processing for Monitoring Children's Health.Computational intelligence and neuroscience · 2022Article
- Evolving Long Short-Term Memory Network-Based Text Classification.Computational intelligence and neuroscience · 2022Article
- Medical Image Captioning Using Optimized Deep Learning Model.Computational intelligence and neuroscience · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A drug-drug interaction or drug synergy is extensively utilised for cancer treatment. However, prediction of drug-drug interaction is defined as an ill-posed problem, because manual testing is only implementable on small group of drugs. Predicting the drug-drug interaction score has been a popular research topic recently. Recently many machine learning models have proposed in the literature to predict the drug-drug interaction score efficiently. However, these models suffer from the over-fitting issue. Therefore, these models are not so-effective for predicting the drug-drug interaction score. In this work, an integrated convolutional mixture density recurrent neural network is proposed and implemented. The proposed model integrates convolutional neural networks, recurrent neural networks and mixture density networks. Extensive comparative analysis reveals that the proposed model significantly outperforms the competitive models.
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Registered trials
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 Socratic graph.