ArticleSensors (Basel, Switzerland)2023
Development and Validation of an Explainable Machine Learning-Based Prediction Model for Drug-Food Interactions from Chemical Structures.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Machine Learning Approaches for Compound-Target Interaction Prediction: A Review.Foods (Basel, Switzerland) · 2026Review
- Artificial Intelligence in Nephrology-State of the Art on Theoretical Background, Molecular Applications, and Clinical Interpretation.International journal of molecular sciences · 2026Review
- Prognostic benefit of preoperative radiotherapy for patients with metastatic larynx and hypopharynx cancers and XGBoost-based prognostic models to predict their survival.Discover oncology · 2026Article
- Machine learning to predict food effects during drug development: a comprehensive review.Journal of cheminformatics · 2025Review
- Peripheral Blood TCR Clonotype Diversity as a Biomarker for Colorectal Cancer.Bioengineering (Basel, Switzerland) · 2025Article
- A comprehensive landscape of AI applications in broad-spectrum drug interaction prediction: a systematic review.Journal of cheminformatics · 2025Review
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- Advancing sepsis diagnosis and immunotherapy machine learning-driven identification of stable molecular biomarkers and therapeutic targets.Scientific reports · 2025Article
- Machine learning selection of basement membrane-associated genes and development of a predictive model for kidney fibrosis.Scientific reports · 2025Article
- Mitigating bias in AI mortality predictions for minority populations: a transfer learning approach.BMC medical informatics and decision making · 2025Article
- Predicting Natural Product-Drug Interactions with Knowledge Graph Embeddings.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- The Impact of Artificial Intelligence on Healthcare: A Comprehensive Review of Advancements in Diagnostics, Treatment, and Operational Efficiency.Health science reports · 2025Article
- Establishment of a novel tumor neoantigen prediction tool for personalized vaccine design.Human vaccines & immunotherapeutics · 2024Article
- Explainable artificial intelligence for stroke prediction through comparison of deep learning and machine learning models.Scientific reports · 2024Article
- RNA-ModX: a multilabel prediction and interpretation framework for RNA modifications.Briefings in bioinformatics · 2024Article
- MetaCGRP is a high-precision meta-model for large-scale identification of CGRP inhibitors using multi-view information.Scientific reports · 2024Article
- RiceSNP-BST: a deep learning framework for predicting biotic stress-associated SNPs in rice.Briefings in bioinformatics · 2024Article
- BertTCR: a Bert-based deep learning framework for predicting cancer-related immune status based on T cell receptor repertoire.Briefings in bioinformatics · 2024Article
- Establishment of prediction model for mortality risk of pancreatic cancer: a retrospective study.BMC medical informatics and decision making · 2024Article
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Authors and funding
5 authors.
Funding
Abstract
Possible drug-food constituent interactions (DFIs) could change the intended efficiency of particular therapeutics in medical practice. The increasing number of multiple-drug prescriptions leads to the rise of drug-drug interactions (DDIs) and DFIs. These adverse interactions lead to other implications, e.g., the decline in medicament's effect, the withdrawals of various medications, and harmful impacts on the patients' health. However, the importance of DFIs remains underestimated, as the number of studies on these topics is constrained. Recently, scientists have applied artificial intelligence-based models to study DFIs. However, there were still some limitations in data mining, input, and detailed annotations. This study proposed a novel prediction model to address the limitations of previous studies. In detail, we extracted 70,477 food compounds from the FooDB database and 13,580 drugs from the DrugBank database. We extracted 3780 features from each drug-food compound pair. The optimal model was eXtreme Gradient Boosting (XGBoost). We also validated the performance of our model on one external test set from a previous study which contained 1922 DFIs. Finally, we applied our model to recommend whether a drug should or should not be taken with some food compounds based on their interactions. The model can provide highly accurate and clinically relevant recommendations, especially for DFIs that may cause severe adverse events and even death. Our proposed model can contribute to developing more robust predictive models to help patients, under the supervision and consultants of physicians, avoid DFI adverse effects in combining drugs and foods for therapy.
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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 Socratic graph.