ArticleBMC bioinformatics2009
RRW: repeated random walks on genome-scale protein networks for local cluster discovery.
Article in BMC bioinformatics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 1 of them a synthesis that pooled it.
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
60 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A new method for identifying causal genes of schizophrenia and anti-tuberculosis drug-induced hepatotoxicity.Scientific reports · 2016Pooled it
- Machine Learning and Deep Learning Frameworks for Human-Virus Protein-Protein Interaction Prediction: Emerging Architectures, Methods, Benchmarks, and Challenges.International journal of molecular sciences · 2026Review
- Network ontology transcript annotation identifies genetic signals underlying sex determination.Scientific reports · 2026Article
- Random walk with restart on multilayer networks: from node prioritisation to supervised link prediction and beyond.BMC bioinformatics · 2024Article
- Structure learning for gene regulatory networks.PLoS computational biology · 2023Article
- Random Walks on Networks with Centrality-Based Stochastic Resetting.Entropy (Basel, Switzerland) · 2023Article
- WLLP: A weighted reconstruction-based linear label propagation algorithm for predicting potential therapeutic agents for COVID-19.Frontiers in microbiology · 2022Article
- Uncovering patterns of the evolution of genomic sequence entropy and complexity.Molecular genetics and genomics : MGG · 2021Article
- Contextualized Protein-Protein Interactions.Patterns (New York, N.Y.) · 2021Article
- A Random Walk-Based Method to Identify Candidate Genes Associated With Lymphoma.Frontiers in genetics · 2021Article
- Use of a Network-Based Method to Identify Latent Genes Associated with Hearing Loss in Children.Frontiers in cell and developmental biology · 2021Article
- CDAP: An Online Package for Evaluation of Complex Detection Methods.Scientific reports · 2019Article
- Integrating data and knowledge to identify functional modules of genes: a multilayer approach.BMC bioinformatics · 2019Article
- Predicting overlapping protein complexes based on core-attachment and a local modularity structure.BMC bioinformatics · 2018Article
- Detection of Protein Complexes Based on Penalized Matrix Decomposition in a Sparse Protein⁻Protein Interaction Network.Molecules (Basel, Switzerland) · 2018Article
- Detecting complexes from edge-weighted PPI networks via genes expression analysis.BMC systems biology · 2018Article
- Improving prediction of heterodimeric protein complexes using combination with pairwise kernel.BMC bioinformatics · 2018Article
- Review of Drug Repositioning Approaches and Resources.International journal of biological sciences · 2018Review
- Determining the minimum number of protein-protein interactions required to support known protein complexes.PloS one · 2018Article
- Predicting protein complexes using a supervised learning method combined with local structural information.PloS one · 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWe propose an efficient and biologically sensitive algorithm based on repeated random walks (RRW) for discovering functional modules, e.g., complexes and pathways, within large-scale protein networks. Compared to existing cluster identification techniques, RRW implicitly makes use of network topology, edge weights, and long range interactions between proteins.
resultsWe apply the proposed technique on a functional network of yeast genes and accurately identify statistically significant clusters of proteins. We validate the biological significance of the results using known complexes in the MIPS complex catalogue database and well-characterized biological processes. We find that 90% of the created clusters have the majority of their catalogued proteins belonging to the same MIPS complex, and about 80% have the majority of their proteins involved in the same biological process. We compare our method to various other clustering techniques, such as the Markov Clustering Algorithm (MCL), and find a significant improvement in the RRW clusters' precision and accuracy values.
conclusionRRW, which is a technique that exploits the topology of the network, is more precise and robust in finding local clusters. In addition, it has the added flexibility of being able to find multi-functional proteins by allowing overlapping clusters.
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.