ArticleScientific reports2017
Machine learning workflow to enhance predictions of Adverse Drug Reactions (ADRs) through drug-gene interactions: application to drugs for cutaneous diseases.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed.
- Predicting DDI-induced pregnancy and neonatal ADRs using sparse PCA and stacking ensemble approach.Journal of integrative bioinformatics · 2025Article
- Role of Artificial Intelligence in Nanomedicine and Organ-specific Therapy: An Updated Review.Current drug targets · 2025Review
- Prediction of adverse drug reactions based on pharmacogenomics combination features: a preliminary study.Frontiers in pharmacology · 2025Article
- Artificial Intelligence and Machine Learning Applications to Pharmacokinetic Modeling and Dose Prediction of Antibiotics: A Scoping Review.Antibiotics (Basel, Switzerland) · 2024Article
- Sex as a Biological Variable in Early-Phase Oncology Clinical Trials: Enhancing the Path to Personalised Medicine.Heliyon · 2024Review
- An extensive survey on the use of supervised machine learning techniques in the past two decades for prediction of drug side effects.Artificial intelligence review · 2023Article
- A Simple Computational Approach to Identify Potential Drugs for Multiple Sclerosis and Cognitive Disorders from Expert Curated Resources.Methods in molecular biology (Clifton, N.J.) · 2022Article
- A Text Mining Protocol for Extracting Drug-Drug Interaction and Adverse Drug Reactions Specific to Patient Population, Pharmacokinetics, Pharmacodynamics, and Disease.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Text Mining Protocol to Retrieve Significant Drug-Gene Interactions from PubMed Abstracts.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Side effect prediction based on drug-induced gene expression profiles and random forest with iterative feature selection.The pharmacogenomics journal · 2021Article
- Deep Modeling of Regulating Effects of Small Molecules on Longevity-Associated Genes.Pharmaceuticals (Basel, Switzerland) · 2021Article
- Artificial intelligence to deep learning: machine intelligence approach for drug discovery.Molecular diversity · 2021Review
- A review on machine learning approaches and trends in drug discovery.Computational and structural biotechnology journal · 2021Review
- Application of Augmented Intelligence for Pharmacovigilance Case Seriousness Determination.Drug safety · 2020Article
- Induced pluripotent stem cells for therapy personalization in pediatric patients: Focus on drug-induced adverse events.World journal of stem cells · 2019Review
- Artificial Intelligence for Drug Toxicity and Safety.Trends in pharmacological sciences · 2019Review
- Drug Repurposing Prediction for Immune-Mediated Cutaneous Diseases using a Word-Embedding-Based Machine Learning Approach.The Journal of investigative dermatology · 2019Article
- Integrative Approach to Reveal Cell Type Specificity and Gene Candidates for Psoriatic Arthritis Outside the MHC.Frontiers in genetics · 2019Article
- Applying Machine Learning Techniques to Identify Undiagnosed Patients with Exocrine Pancreatic Insufficiency.Journal of health economics and outcomes research · 2019Article
- Deep learning improves prediction of drug-drug and drug-food interactions.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Adverse drug reactions (ADRs) pose critical public health issues, affecting over 6% of hospitalized patients. While knowledge of potential drug-drug interactions (DDI) is necessary to prevent ADR, the rapid pace of drug discovery makes it challenging to maintain a strong insight into DDIs. In this study, we present a novel literature-mining framework for enhancing the predictions of DDIs and ADR types by integrating drug-gene interactions (DGIs). The ADR types were adapted from a DDI corpus, including i) adverse effect; ii) effect at molecular level; iii) effect related to pharmacokinetics; and iv) DDIs without known ADRs. By using random forest classifier our approach achieves an F-score of 0.87 across the ADRs classification using only the DDI features. We then enhanced the performance of the classifier by including DGIs (F-score = 0.90), and applied the classification model trained with the DDI corpus to identify the drugs that might interact with the drugs for cutaneous diseases. We successfully predict previously known ADRs for drugs prescribed to cutaneous diseases, and are also able to identify promising new ADRs.
Indexed as
Identifiers
What OpenQuestion holds
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.