ArticleComputational and mathematical methods in medicine2022
Drug-Drug Interactions Prediction Using Fingerprint Only.
Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.
- Computational approaches for drug-drug interaction prediction: a systematic review of data sources, modeling strategies, and evaluation frameworks.Frontiers in pharmacology · 2026Pooled it
- Generalizable machine learning models for rapid antimicrobial resistance prediction in unseen health care settings.GigaScience · 2026Article
- Drug-drug interaction prediction of traditional Chinese medicine based on graph attention networks.Scientific reports · 2025Article
- Identification of Phase-Separation-Protein-Related Function Based on Gene Ontology by Using Machine Learning Methods.Life (Basel, Switzerland) · 2023Article
- Machine Learning Classification of Time since BNT162b2 COVID-19 Vaccination Based on Array-Measured Antibody Activity.Life (Basel, Switzerland) · 2023Article
- Identification of Genes Associated with the Impairment of Olfactory and Gustatory Functions in COVID-19 via Machine-Learning Methods.Life (Basel, Switzerland) · 2023Article
- Identification of Smoking-Associated Transcriptome Aberration in Blood with Machine Learning Methods.BioMed research international · 2023Article
- Characterization of chromatin accessibility patterns in different mouse cell types using machine learning methods at single-cell resolution.Frontiers in genetics · 2023Article
- Identification of genes related to immune enhancement caused by heterologous ChAdOx1-BNT162b2 vaccines in lymphocytes at single-cell resolution with machine learning methods.Frontiers in immunology · 2023Article
- Identification of dynamic gene expression profiles during sequential vaccination with ChAdOx1/BNT162b2 using machine learning methods.Frontiers in microbiology · 2023Article
- Immune responses of different COVID-19 vaccination strategies by analyzing single-cell RNA sequencing data from multiple tissues using machine learning methods.Frontiers in genetics · 2023Article
- Identification of Transcriptome Biomarkers for Severe COVID-19 with Machine Learning Methods.Biomolecules · 2022Article
- Identifying MicroRNA Markers That Predict COVID-19 Severity Using Machine Learning Methods.Life (Basel, Switzerland) · 2022Article
- Identification of COVID-19 severity biomarkers based on feature selection on single-cell RNA-Seq data of CD8Frontiers in genetics · 2022Article
- Identification of methylation signatures and rules for predicting the severity of SARS-CoV-2 infection with machine learning methods.Frontiers in microbiology · 2022Article
- Identification of protein-protein interaction associated functions based on gene ontology and KEGG pathway.Frontiers in genetics · 2022Article
- Characterization of spleen and lymph node cell typesFrontiers in molecular neuroscience · 2022Article
- Identification of Methylation Signatures and Rules for Sarcoma Subtypes by Machine Learning Methods.BioMed research international · 2022Article
- Identifying anal and cervical tumorigenesis-associated methylation signaling with machine learning methods.Frontiers in oncology · 2022Article
- Identification of COVID-19-Specific Immune Markers Using a Machine Learning Method.Frontiers in molecular biosciences · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Combination drug therapy is an efficient way to treat complicated diseases. Drug-drug interaction (DDI) is an important research topic in this therapy as patient safety is a problem when two or more drugs are taken at the same time. Traditionally, in vitro experiments and clinical trials are common ways to determine DDIs. However, these methods cannot meet the requirements of large-scale tests. It is an alternative way to develop computational methods for predicting DDIs. Although several previous methods have been proposed, they always need several types of drug information, limiting their applications. In this study, we proposed a simple computational method to predict DDIs. In this method, drugs were represented by their fingerprint features, which are most widely used in investigating drug-related problems. These features were refined by three models, including addition, subtraction, and Hadamard models, to generate the representation of DDIs. The powerful classification algorithm, random forest, was picked up to build the classifier. The results of two types of tenfold cross-validation on the classifier indicated good performance for discovering novel DDIs among known drugs and acceptable performance for identifying DDIs between known drugs and unknown drugs or among unknown drugs. Although the classifier adopted a sample scheme to represent DDIs, it was still superior to other methods, which adopted features generated by some advanced computer algorithms. Furthermore, a user-friendly web-server, named DDIPF (http://106.14.164.77:5004/DDIPF/), was developed to implement the classifier.
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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 OpenQuestion graph.