ArticleFrontiers in genetics2020
Prioritizing Cancer Genes Based on an Improved Random Walk Method.
Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 32 citations in OpenAlex.
- Enhancing Molecular Network-Based Cancer Driver Gene Prediction Using Machine Learning Approaches: Current Challenges and Opportunities.Journal of cellular and molecular medicine · 2025Review
- Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks.Briefings in bioinformatics · 2024Review
- Logic-based modeling and drug repurposing for the prediction of novel therapeutic targets and combination regimens against E2F1-driven melanoma progression.BMC chemistry · 2023Article
- Network medicine: an approach to complex kidney disease phenotypes.Nature reviews. Nephrology · 2023Review
- Ranking Plant Network Nodes Based on Their Centrality Measures.Entropy (Basel, Switzerland) · 2023Article
- Identification of key genes and therapeutic drugs for cocaine addiction using integrated bioinformatics analysis.Frontiers in neuroscience · 2023Article
- A novel candidate disease gene prioritization method using deep graph convolutional networks and semi-supervised learning.BMC bioinformatics · 2022Article
- Data analysis methods for defining biomarkers from omics data.Analytical and bioanalytical chemistry · 2022Review
- Comparative analysis of aneurysm subtypes associated genes based on protein-protein interaction network.BMC bioinformatics · 2021Article
- An Efficient and Easy-to-Use Network-Based Integrative Method of Multi-Omics Data for Cancer Genes Discovery.Frontiers in genetics · 2020Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying driver genes that contribute to cancer progression from numerous passenger genes, although a central goal, is a major challenge. The protein-protein interaction network provides convenient and reasonable assistance for driver gene discovery. Random walk-based methods have been widely used to prioritize nodes in social or biological networks. However, most studies select the next arriving node uniformly from the random walker's neighbors. Few consider transiting preference according to the degree of random walker's neighbors. In this study, based on the random walk method, we propose a novel approach named Driver_IRW (Driver genes discovery with Improved Random Walk method), to prioritize cancer genes in cancer-related network. The key idea of Driver_IRW is to assign different transition probabilities for different edges of a constructed cancer-related network in accordance with the degree of the nodes' neighbors. Furthermore, the global centrality (here is betweenness centrality) and Katz feedback centrality are incorporated into the framework to evaluate the probability to walk to the seed nodes. Experimental results on four cancer types indicate that Driver_IRW performs more efficiently than some previously published methods for uncovering known cancer-related genes. In conclusion, our method can aid in prioritizing cancer-related genes and complement traditional frequency and network-based methods.
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