Ranking cancer drivers via betweenness-based outlier detection and random walks
Abstract Background Recent cancer genomic studies have generated detailed molecular data on a large number of cancer patients. A key remaining problem in cancer genomics is the identification of driver genes. Results We propose BetweenNet, a computational approach that integrates genomic data with a...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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BMC
2021-02-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-021-03989-w |
| _version_ | 1818870623568396288 |
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| author | Cesim Erten Aissa Houdjedj Hilal Kazan |
| author_facet | Cesim Erten Aissa Houdjedj Hilal Kazan |
| author_sort | Cesim Erten |
| collection | DOAJ |
| description | Abstract Background Recent cancer genomic studies have generated detailed molecular data on a large number of cancer patients. A key remaining problem in cancer genomics is the identification of driver genes. Results We propose BetweenNet, a computational approach that integrates genomic data with a protein-protein interaction network to identify cancer driver genes. BetweenNet utilizes a measure based on betweenness centrality on patient specific networks to identify the so-called outlier genes that correspond to dysregulated genes for each patient. Setting up the relationship between the mutated genes and the outliers through a bipartite graph, it employs a random-walk process on the graph, which provides the final prioritization of the mutated genes. We compare BetweenNet against state-of-the art cancer gene prioritization methods on lung, breast, and pan-cancer datasets. Conclusions Our evaluations show that BetweenNet is better at recovering known cancer genes based on multiple reference databases. Additionally, we show that the GO terms and the reference pathways enriched in BetweenNet ranked genes and those that are enriched in known cancer genes overlap significantly when compared to the overlaps achieved by the rankings of the alternative methods. |
| first_indexed | 2024-12-19T12:09:58Z |
| format | Article |
| id | doaj.art-b19b7e103c444db8bb2f876ff5a2b573 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-19T12:09:58Z |
| publishDate | 2021-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-b19b7e103c444db8bb2f876ff5a2b5732022-12-21T20:22:13ZengBMCBMC Bioinformatics1471-21052021-02-0122111610.1186/s12859-021-03989-wRanking cancer drivers via betweenness-based outlier detection and random walksCesim Erten0Aissa Houdjedj1Hilal Kazan2Department of Computer Engineering, Antalya Bilim UniversityElectrical and Computer Engineering Graduate Program, Antalya Bilim UniversityDepartment of Computer Engineering, Antalya Bilim UniversityAbstract Background Recent cancer genomic studies have generated detailed molecular data on a large number of cancer patients. A key remaining problem in cancer genomics is the identification of driver genes. Results We propose BetweenNet, a computational approach that integrates genomic data with a protein-protein interaction network to identify cancer driver genes. BetweenNet utilizes a measure based on betweenness centrality on patient specific networks to identify the so-called outlier genes that correspond to dysregulated genes for each patient. Setting up the relationship between the mutated genes and the outliers through a bipartite graph, it employs a random-walk process on the graph, which provides the final prioritization of the mutated genes. We compare BetweenNet against state-of-the art cancer gene prioritization methods on lung, breast, and pan-cancer datasets. Conclusions Our evaluations show that BetweenNet is better at recovering known cancer genes based on multiple reference databases. Additionally, we show that the GO terms and the reference pathways enriched in BetweenNet ranked genes and those that are enriched in known cancer genes overlap significantly when compared to the overlaps achieved by the rankings of the alternative methods.https://doi.org/10.1186/s12859-021-03989-wDriver gene prioritizationBipartite graphBetweenness centralityNetwork diffusion |
| spellingShingle | Cesim Erten Aissa Houdjedj Hilal Kazan Ranking cancer drivers via betweenness-based outlier detection and random walks BMC Bioinformatics Driver gene prioritization Bipartite graph Betweenness centrality Network diffusion |
| title | Ranking cancer drivers via betweenness-based outlier detection and random walks |
| title_full | Ranking cancer drivers via betweenness-based outlier detection and random walks |
| title_fullStr | Ranking cancer drivers via betweenness-based outlier detection and random walks |
| title_full_unstemmed | Ranking cancer drivers via betweenness-based outlier detection and random walks |
| title_short | Ranking cancer drivers via betweenness-based outlier detection and random walks |
| title_sort | ranking cancer drivers via betweenness based outlier detection and random walks |
| topic | Driver gene prioritization Bipartite graph Betweenness centrality Network diffusion |
| url | https://doi.org/10.1186/s12859-021-03989-w |
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