ArticleScientific reports2019
Discovering Links Between Side Effects and Drugs Using a Diffusion Based Method.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- PromptSE: drug side effect prediction with LLM-derived pharmacological representations.Scientific reports · 2026Article
- Biology-Informed Matrix Factorization: An AI-Driven Framework for Enhanced Drug Repositioning.Biology · 2025Article
- A Fusion Deep Learning Model for Predicting Adverse Drug Reactions Based on Multiple Drug Characteristics.Life (Basel, Switzerland) · 2025Article
- Know your molecule: pharmacological characterization of drug candidates to enhance efficacy and reduce late-stage attrition.Nature reviews. Drug discovery · 2024Review
- A general hypergraph learning algorithm for drug multi-task predictions in micro-to-macro biomedical networks.PLoS computational biology · 2023Article
- iADRGSE: A Graph-Embedding and Self-Attention Encoding for Identifying Adverse Drug Reaction in the Earlier Phase of Drug Development.International journal of molecular sciences · 2022Article
- Predicting adverse drug effects: A heterogeneous graph convolution network with a multi-layer perceptron approach.PloS one · 2022Article
- Adverse effect signature extraction and prediction for drugs treating COVID-19.Frontiers in genetics · 2022Article
- Induced pluripotent stem cells as an innovative model to study drug induced pancreatitis.World journal of gastroenterology · 2021Article
- Discovering symptom patterns of COVID-19 patients using association rule mining.Computers in biology and medicine · 2021Article
- Semi-supervised Hierarchical Drug Embedding in Hyperbolic Space.Journal of chemical information and modeling · 2020Article
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
Identifying the unintended effects of drugs (side effects) is a very important issue in pharmacological studies. The laboratory verification of associations between drugs and side effects requires costly, time-intensive research. Thus, an approach to predicting drug side effects based on known side effects, using a computational model, is highly desirable. To provide such a model, we used openly available data resources to model drugs and side effects as a bipartite graph. The drug-drug network is constructed using the word2vec model where the edges between drugs represent the semantic similarity between them. We integrated the bipartite graph and the semantic similarity graph using a matrix factorization method and a diffusion based model. Our results show the effectiveness of this integration by computing weighted (i.e., ranked) predictions of initially unknown links between side effects and drugs.
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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.