ArticleThe pharmacogenomics journal2021
Side effect prediction based on drug-induced gene expression profiles and random forest with iterative feature selection.
Article in The pharmacogenomics journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Integrating Pharmacogenomics and Network Topology for Machine Learning Prediction of HLA-Associated Severe Cutaneous Adverse Drug Reactions.International journal of molecular sciences · 2026Article
- Explainable drug side effect prediction in central neural system via biologically informed graph neural network.Translational psychiatry · 2026Article
- AGRL-DSE: Adaptive Graph Representation Learning on a Heterogeneous Graph for Drug Side Effect Prediction.ACS omega · 2025Article
- Artificial Intelligence-Driven Drug Toxicity Prediction: Advances, Challenges, and Future Directions.Toxics · 2025Review
- Graph reasoning method enhanced by relational transformers and knowledge distillation for drug-related side effect prediction.iScience · 2024Article
- Learning Multi-Types of Neighbor Node Attributes and Semantics by Heterogeneous Graph Transformer and Multi-View Attention for Drug-Related Side-Effect Prediction.Molecules (Basel, Switzerland) · 2023Article
- A neighborhood-regularization method leveraging multiview data for predicting the frequency of drug-side effects.Bioinformatics (Oxford, England) · 2023Article
- Article
- Graph generative and adversarial strategy-enhanced node feature learning and self-calibrated pairwise attribute encoding for prediction of drug-related side effects.Frontiers in pharmacology · 2023Article
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
No grant is acknowledged in the PubMed record.
Abstract
One in every ten drug candidates fail in clinical trials mainly due to efficacy and safety related issues, despite in-depth preclinical testing. Even some of the approved drugs such as chemotherapeutics are notorious for their side effects that are burdensome on patients. In order to pave the way for new therapeutics with more tolerable side effects, the mechanisms underlying side effects need to be fully elucidated. In this work, we addressed the common side effects of chemotherapeutics, namely alopecia, diarrhea and edema. A strategy based on Random Forest algorithm unveiled an expression signature involving 40 genes that predicted these side effects with an accuracy of 89%. We further characterized the resulting signature and its association with the side effects using functional enrichment analysis and protein-protein interaction networks. This work contributes to the ongoing efforts in drug development for early identification of side effects to use the resources more effectively.
Indexed as
Identifiers
34155353What 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.