ArticlePloS one2017
An integrated method for the identification of novel genes related to oral cancer.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Integrated multiplex network based approach for hub gene identification in oral cancer.Heliyon · 2021Article
- 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
- Inferring Novel Tumor Suppressor Genes with a Protein-Protein Interaction Network and Network Diffusion Algorithms.Molecular therapy. Methods & clinical development · 2018Article
- Special Protein Molecules Computational Identification.International journal of molecular sciences · 2018Article
- A computational method using the random walk with restart algorithm for identifying novel epigenetic factors.Molecular genetics and genomics : MGG · 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
- Deciphering the Relationship between Obesity and Various Diseases from a Network Perspective.Genes · 2017Article
- Identifying and analyzing different cancer subtypes using RNA-seq data of blood platelets.Oncotarget · 2017Article
- Network-Based Method for Identifying Co- Regeneration Genes in Bone, Dentin, Nerve and Vessel Tissues.Genes · 2017Article
- Article
- Identifying novel fruit-related genes in Arabidopsis thaliana based on the random walk with restart algorithm.PloS one · 2017Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer is a significant public health problem worldwide. Complete identification of genes related to one type of cancer facilitates earlier diagnosis and effective treatments. In this study, two widely used algorithms, the random walk with restart algorithm and the shortest path algorithm, were adopted to construct two parameterized computational methods, namely, an RWR-based method and an SP-based method; based on these methods, an integrated method was constructed for identifying novel disease genes. To validate the utility of the integrated method, data for oral cancer were used, on which the RWR-based and SP-based methods were trained, thereby building two optimal methods. The integrated method combining these optimal methods was further adopted to identify the novel genes of oral cancer. As a result, 85 novel genes were inferred, among which eleven genes (e.g., MYD88, FGFR2, NF-κBIA) were identified by both the RWR-based and SP-based methods, 70 genes (e.g., BMP4, IFNG, KITLG) were discovered only by the RWR-based method and four genes (L1R1, MCM6, NOG and CXCR3) were predicted only by the SP-based method. Extensive analyses indicate that several novel genes have strong associations with cancers, indicating the effectiveness of the integrated method for identifying disease genes.
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