Related Experiment Video
Updated: Aug 15, 2025
A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
Published on: October 13, 2023
Large-scale prediction of adverse drug reactions-related proteins with network embedding
Jaesub Park1, Sangyeon Lee1, Kwansoo Kim1
1Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, South Korea.
Motivation:
Adverse drug reactions (ADRs) are a major issue in drug development and clinical pharmacology. As most ADRs are caused by unintended activity at off-targets of drugs, the identification of drug targets responsible for ADRs becomes a key process for resolving ADRs. Recently, with the increase in the number of ADR-related data sources, several computational methodologies have been proposed to analyze ADR-protein relations. However, the identification of ADR-related proteins on a large scale with high reliability remains an important challenge.
Results:
In this article, we suggest a computational approach, Large-scale ADR-related Proteins Identification with Network Embedding (LAPINE). LAPINE combines a novel concept called single-target compound with a network embedding technique to enable large-scale prediction of ADR-related proteins for any proteins in the protein-protein interaction network. Analysis of benchmark datasets confirms the need to expand the scope of potential ADR-related proteins to be analyzed, as well as LAPINE's capability for high recovery of known ADR-related proteins. Moreover, LAPINE provides more reliable predictions for ADR-related proteins (Value-added positive predictive value = 0.12), compared to a previously proposed method (P < 0.001). Furthermore, two case studies show that most predictive proteins related to ADRs in LAPINE are supported by literature evidence. Overall, LAPINE can provide reliable insights into the relationship between ADRs and proteomes to understand the mechanism of ADRs leading to their prevention.
Availability And Implementation:
The source code is available at GitHub (https://github.com/rupinas/LAPINE) and Figshare (https://figshare.com/articles/software/LAPINE/21750245) to facilitate its use.
Supplementary Information:
Supplementary data are available at Bioinformatics online.
More Related Videos
08:31Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
Published on: December 1, 2020
07:28JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
Published on: October 19, 2021
Related Concept Videos
02:26Protein Networks
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
02:04Protein-protein Interfaces
01:19Pharmacovigilance
This process, termed pharmacovigilance, aims to detect, evaluate, and minimize harmful effects related to medication use. The data collection for pharmacovigilance depends on spontaneous reporting systems, where healthcare professionals or patients voluntarily report suspected ADRs.
In some cases, there...
02:24Predicting Reaction Outcomes
01:20Factors Affecting Protein-Drug Binding: Protein-Related Factors
The physicochemical properties of a drug play a significant role in its ability to bind to proteins. Lipophilic drugs, which dissolve in fats, oils, and lipids, can be...
01:18Factors Affecting Protein-Drug Binding: Drug-Related Factors
One crucial factor in drug-protein binding is the drug's lipophilicity or its affinity for fat. More lipophilic drugs tend to have higher binding extents. For example, highly lipophilic drugs like cloxacillin exhibit substantial protein binding, with as much as 95% of the drug binding to proteins. In...