ArticleFrontiers in pharmacology2019
A Comparative Study of Cluster Detection Algorithms in Protein-Protein Interaction for Drug Target Discovery and Drug Repurposing.
Article in Frontiers in pharmacology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 46 citations in OpenAlex.
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- Harnessing AI to fuse phenotypic signatures for drug target identification: progress in computational modeling.Briefings in bioinformatics · 2026Review
- Literature data-based de novo candidates for drug repurposing.BMC bioinformatics · 2025Article
- Molecular basis identification and hypnotic drug interactions for cognitive impairment related to sleep deprivation.BMC psychiatry · 2025Article
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- COVID-19: A novel holistic systems biology approach to predict its molecular mechanisms (in vitro) and repurpose drugs.Daru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences · 2023Article
- Artificial Intelligence, Machine Learning, and Big Data for Ebola Virus Drug Discovery.Pharmaceuticals (Basel, Switzerland) · 2023Article
- Informatics on Drug Repurposing for Breast Cancer.Drug design, development and therapy · 2023Review
- LazyFox: fast and parallelized overlapping community detection in large graphs.PeerJ. Computer science · 2023Article
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- NEXGB: A Network Embedding Framework for Anticancer Drug Combination Prediction.International journal of molecular sciences · 2022Article
- Protein complexes detection based on node local properties and gene expression in PPI weighted networks.BMC bioinformatics · 2022Article
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- Repurposing new drug candidates and identifying crucial molecules underlying PCOS Pathogenesis Based On Bioinformatics Analysis.Daru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences · 2021Article
- SIGNAL: A web-based iterative analysis platform integrating pathway and network approaches optimizes hit selection from genome-scale assays.Cell systems · 2021Article
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- DLS: A Link Prediction Method Based on Network Local Structure for Predicting Drug-Protein Interactions.Frontiers in bioengineering and biotechnology · 2020Article
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Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The interactions between drugs and their target proteins induce altered expression of genes involved in complex intracellular networks. The properties of these functional network modules are critical for the identification of drug targets, for drug repurposing, and for understanding the underlying mode of action of the drug. The topological modules generated by a computational approach are defined as functional clusters. However, the functions inferred for these topological modules extracted from a large-scale molecular interaction network, such as a protein-protein interaction (PPI) network, could differ depending on different cluster detection algorithms. Moreover, the dynamic gene expression profiles among tissues or cell types causes differential functional interaction patterns between the molecular components. Thus, the connections in the PPI network should be modified by the transcriptomic landscape of specific cell lines before producing topological clusters. Here, we systematically investigated the clusters of a cell-based PPI network by using four cluster detection algorithms. We subsequently compared the performance of these algorithms for target gene prediction, which integrates gene perturbation data with the cell-based PPI network using two drug target prioritization methods, shortest path and diffusion correlation. In addition, we validated the proportion of perturbed genes in clusters by finding candidate anti-breast cancer drugs and confirming our predictions using literature evidence and cases in the ClinicalTrials.gov. Our results indicate that the Walktrap (CW) clustering algorithm achieved the best performance overall in our comparative study.
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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.