ArticleBMC genomics2012
Disease gene identification by random walk on multigraphs merging heterogeneous genomic and phenotype data.
Article in BMC genomics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- MKLNID: Identifying Melanoma-related Pathogenic Genes Through Multiple Kernel Learning and Network Impulsive Dynamics.Interdisciplinary sciences, computational life sciences · 2026Article
- DyNDG: Identifying Leukemia-related Genes Based on Time-series Dynamic Network by Integrating Differential Genes.Genomics, proteomics & bioinformatics · 2025Article
- A novel candidate disease gene prioritization method using deep graph convolutional networks and semi-supervised learning.BMC bioinformatics · 2022Article
- Unsupervised Multi-Omics Data Integration Methods: A Comprehensive Review.Frontiers in genetics · 2022Review
- Identification of Therapeutic Targets for the Selective Killing of HBV-Positive Hepatocytes.Journal of personalized medicine · 2021Article
- System-Level Analysis of Alzheimer's Disease Prioritizes Candidate Genes for Neurodegeneration.Frontiers in genetics · 2021Article
- Graph Neural Networks and Their Current Applications in Bioinformatics.Frontiers in genetics · 2021Article
- Identification of Early Warning Signals at the Critical Transition Point of Colorectal Cancer Based on Dynamic Network Analysis.Frontiers in bioengineering and biotechnology · 2020Article
- Identifying Cancer genes by combining two-rounds RWR based on multiple biological data.BMC bioinformatics · 2019Article
- Article
- Use of Laplacian Heat Diffusion Algorithm to Infer Novel Genes With Functions Related to Uveitis.Frontiers in genetics · 2018Article
- Refine gene functional similarity network based on interaction networks.BMC bioinformatics · 2017Article
- An enhanced topologically significant directed random walk in cancer classification using gene expression datasets.Saudi journal of biological sciences · 2017Article
- Constructing an integrated gene similarity network for the identification of disease genes.Journal of biomedical semantics · 2017Article
- Determination of Genes Related to Uveitis by Utilization of the Random Walk with Restart Algorithm on a Protein-Protein Interaction Network.International journal of molecular sciences · 2017Article
- A large-scale benchmark of gene prioritization methods.Scientific reports · 2017Article
- m6A-Driver: Identifying Context-Specific mRNA m6A Methylation-Driven Gene Interaction Networks.PLoS computational biology · 2016Article
- Network analysis reveals potential markers for pediatric adrenocortical carcinoma.OncoTargets and therapy · 2016Article
- Integration of Multiple Genomic and Phenotype Data to Infer Novel miRNA-Disease Associations.PloS one · 2016Article
- Heterogeneous Network Edge Prediction: A Data Integration Approach to Prioritize Disease-Associated Genes.PLoS computational biology · 2015Article
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2 authors.
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Abstract
backgroundHigh throughput experiments resulted in many genomic datasets and hundreds of candidate disease genes. To discover the real disease genes from a set of candidate genes, computational methods have been proposed and worked on various types of genomic data sources. As a single source of genomic data is prone of bias, incompleteness and noise, integration of different genomic data sources is highly demanded to accomplish reliable disease gene identification.
resultsIn contrast to the commonly adapted data integration approach which integrates separate lists of candidate genes derived from the each single data sources, we merge various genomic networks into a multigraph which is capable of connecting multiple edges between a pair of nodes. This novel approach provides a data platform with strong noise tolerance to prioritize the disease genes. A new idea of random walk is then developed to work on multigraphs using a modified step to calculate the transition matrix. Our method is further enhanced to deal with heterogeneous data types by allowing cross-walk between phenotype and gene networks. Compared on benchmark datasets, our method is shown to be more accurate than the state-of-the-art methods in disease gene identification. We also conducted a case study to identify disease genes for Insulin-Dependent Diabetes Mellitus. Some of the newly identified disease genes are supported by recently published literature.
conclusionsThe proposed RWRM (Random Walk with Restart on Multigraphs) model and CHN (Complex Heterogeneous Network) model are effective in data integration for candidate gene prioritization.
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