ArticleMolecular genetics and genomics : MGG2018
A computational method using the random walk with restart algorithm for identifying novel epigenetic factors.
Article in Molecular genetics and genomics : MGG, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
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- Network analysis of atherosclerotic genes elucidates druggable targets.BMC medical genomics · 2022Article
- Deep semi-supervised learning ensemble framework for classifying co-mentions of human proteins and phenotypes.BMC bioinformatics · 2021Article
- Candidate gene prioritization for chronic obstructive pulmonary disease using expression information in protein-protein interaction networks.BMC pulmonary medicine · 2021Article
- Personalized single-cell networks: a framework to predict the response of any gene to any drug for any patient.BioData mining · 2021Article
- A Random Walk-Based Method to Identify Candidate Genes Associated With Lymphoma.Frontiers in genetics · 2021Article
- A deep learning model for plant lncRNA-protein interaction prediction with graph attention.Molecular genetics and genomics : MGG · 2020Article
- Investigation of miRNA and mRNA Co-expression Network in Ependymoma.Frontiers in bioengineering and biotechnology · 2020Article
- Inferring novel genes related to oral cancer with a network embedding method and one-class learning algorithms.Gene therapy · 2019Article
- Inferring novel genes related to colorectal cancer via random walk with restart algorithm.Gene therapy · 2019Article
- A novel lncRNA-miRNA-mRNA network analysis identified the hub lncRNA RP11-159F24.1 in the pathogenesis of papillary thyroid cancer.Cancer medicine · 2018Article
- Inferring Novel Tumor Suppressor Genes with a Protein-Protein Interaction Network and Network Diffusion Algorithms.Molecular therapy. Methods & clinical development · 2018Article
- Cross talk of chromosome instability, CpG island methylator phenotype and mismatch repair in colorectal cancer.Oncology letters · 2018Article
- INTERSPIA: a web application for exploring the dynamics of protein-protein interactions among multiple species.Nucleic acids research · 2018Article
- Computational Approach to Investigating Key GO Terms and KEGG Pathways Associated with CNV.BioMed research international · 2018Article
- Use of Laplacian Heat Diffusion Algorithm to Infer Novel Genes With Functions Related to Uveitis.Frontiers in genetics · 2018Article
- Identification of tumor-educated platelet biomarkers of non-small-cell lung cancer.OncoTargets and therapy · 2018Article
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Authors and funding
7 authors.
Funding
Abstract
Epigenetic regulation has long been recognized as a significant factor in various biological processes, such as development, transcriptional regulation, spermatogenesis, and chromosome stabilization. Epigenetic alterations lead to many human diseases, including cancer, depression, autism, and immune system defects. Although efforts have been made to identify epigenetic regulators, it remains a challenge to systematically uncover all the components of the epigenetic regulation in the genome level using experimental approaches. The advances of constructing protein-protein interaction (PPI) networks provide an excellent opportunity to identify novel epigenetic factors computationally in the genome level. In this study, we identified potential epigenetic factors by using a computational method that applied the random walk with restart (RWR) algorithm on a protein-protein interaction (PPI) network using reported epigenetic factors as seed nodes. False positives were identified by their specific roles in the PPI network or by a low-confidence interaction and a weak functional relationship with epigenetic regulators. After filtering out the false positives, 26 candidate epigenetic factors were finally accessed. According to previous studies, 22 of these are thought to be involved in epigenetic regulation, suggesting the robustness of our method. Our study provides a novel computational approach which successfully identified 26 potential epigenetic factors, paving the way on deepening our understandings on the epigenetic mechanism.
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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.