Disease-related gene prioritization is one of the most well-established pharmaceutical techniques used to identify genes that are important to a biological process relevant to a disease. In identifying these essential genes, the network diffusion (ND) approach is a widely used technique applied in gene prioritization. However, there is still a large number of candidate genes that need to be evaluated experimentally. Therefore, it would be of great value to develop a new strategy to improve the precision of the prioritization. Given the efficiency and simplicity of centrality measures in capturing a gene that might be important to the network structure, herein, we propose a technique that extends the scope of ND through a centrality measure to identify new disease-related genes. Five common centrality measures with different aspects were examined for integration in the traditional ND model. A total of 40 diseases were used to test our developed approach and to find new genes that might be related to a disease. Results indicated that the best measure to combine with the diffusion is closeness centrality. The novel candidate genes identified by the model for all 40 diseases were provided along with supporting evidence. In conclusion, the integration of network centrality in ND is a simple but effective technique to discover more precise disease-related genes, which is extremely useful for biomedical science.
Citation: Panisa Janyasupab, Apichat Suratanee, Kitiporn Plaimas. Network diffusion with centrality measures to identify disease-related genes[J]. Mathematical Biosciences and Engineering, 2021, 18(3): 2909-2929. doi: 10.3934/mbe.2021147
| [1] | Zhenliang Zhu, Yuming Chen, Zhong Li, Fengde Chen . Dynamic behaviors of a Leslie-Gower model with strong Allee effect and fear effect in prey. Mathematical Biosciences and Engineering, 2023, 20(6): 10977-10999. doi: 10.3934/mbe.2023486 |
| [2] | Mengyun Xing, Mengxin He, Zhong Li . Dynamics of a modified Leslie-Gower predator-prey model with double Allee effects. Mathematical Biosciences and Engineering, 2024, 21(1): 792-831. doi: 10.3934/mbe.2024034 |
| [3] | Kawkab Al Amri, Qamar J. A Khan, David Greenhalgh . Combined impact of fear and Allee effect in predator-prey interaction models on their growth. Mathematical Biosciences and Engineering, 2024, 21(10): 7211-7252. doi: 10.3934/mbe.2024319 |
| [4] | Yun Kang, Sourav Kumar Sasmal, Amiya Ranjan Bhowmick, Joydev Chattopadhyay . Dynamics of a predator-prey system with prey subject to Allee effects and disease. Mathematical Biosciences and Engineering, 2014, 11(4): 877-918. doi: 10.3934/mbe.2014.11.877 |
| [5] | Yuhong Huo, Gourav Mandal, Lakshmi Narayan Guin, Santabrata Chakravarty, Renji Han . Allee effect-driven complexity in a spatiotemporal predator-prey system with fear factor. Mathematical Biosciences and Engineering, 2023, 20(10): 18820-18860. doi: 10.3934/mbe.2023834 |
| [6] | Yongli Cai, Malay Banerjee, Yun Kang, Weiming Wang . Spatiotemporal complexity in a predator--prey model with weak Allee effects. Mathematical Biosciences and Engineering, 2014, 11(6): 1247-1274. doi: 10.3934/mbe.2014.11.1247 |
| [7] | Manoj K. Singh, Brajesh K. Singh, Poonam, Carlo Cattani . Under nonlinear prey-harvesting, effect of strong Allee effect on the dynamics of a modified Leslie-Gower predator-prey model. Mathematical Biosciences and Engineering, 2023, 20(6): 9625-9644. doi: 10.3934/mbe.2023422 |
| [8] | Shuangte Wang, Hengguo Yu . Stability and bifurcation analysis of the Bazykin's predator-prey ecosystem with Holling type Ⅱ functional response. Mathematical Biosciences and Engineering, 2021, 18(6): 7877-7918. doi: 10.3934/mbe.2021391 |
| [9] | Fang Liu, Yanfei Du . Spatiotemporal dynamics of a diffusive predator-prey model with delay and Allee effect in predator. Mathematical Biosciences and Engineering, 2023, 20(11): 19372-19400. doi: 10.3934/mbe.2023857 |
| [10] | Juan Ye, Yi Wang, Zhan Jin, Chuanjun Dai, Min Zhao . Dynamics of a predator-prey model with strong Allee effect and nonconstant mortality rate. Mathematical Biosciences and Engineering, 2022, 19(4): 3402-3426. doi: 10.3934/mbe.2022157 |
Disease-related gene prioritization is one of the most well-established pharmaceutical techniques used to identify genes that are important to a biological process relevant to a disease. In identifying these essential genes, the network diffusion (ND) approach is a widely used technique applied in gene prioritization. However, there is still a large number of candidate genes that need to be evaluated experimentally. Therefore, it would be of great value to develop a new strategy to improve the precision of the prioritization. Given the efficiency and simplicity of centrality measures in capturing a gene that might be important to the network structure, herein, we propose a technique that extends the scope of ND through a centrality measure to identify new disease-related genes. Five common centrality measures with different aspects were examined for integration in the traditional ND model. A total of 40 diseases were used to test our developed approach and to find new genes that might be related to a disease. Results indicated that the best measure to combine with the diffusion is closeness centrality. The novel candidate genes identified by the model for all 40 diseases were provided along with supporting evidence. In conclusion, the integration of network centrality in ND is a simple but effective technique to discover more precise disease-related genes, which is extremely useful for biomedical science.
| [1] |
A.-L. Barabási, N. Gulbahce, J. Loscalzo, Network medicine: A network-based approach to human disease, Nat. Rev. Genet., 12 (2011), 56-68. doi: 10.1038/nrg2918
|
| [2] |
M. Caldera, P. Buphamalai, F. Mueller, J. Menche, Interactome-based approaches to human disease, Curr. Opin. Syst. Biol., 3 (2017), 88-94. doi: 10.1016/j.coisb.2017.04.015
|
| [3] | E. K. Silverman, H. Schmidt, E. Anastasiadou, L. Altucci, M. Angelini, L. Badimon, et al., Molecular networks in network medicine: development and applications, Wiley Interdiscip. Rev. Syst. Biol. Med., 12 (2020), e1489. |
| [4] |
P. Paci, G. Fiscon, F. Conte, R.-S. Wang, L. Farina, J. Loscalzo, Gene co-expression in the interactome: moving from correlation toward causation via an integrated approach to disease module discovery, NPJ Syst. Biol. Appl., 7 (2021), 3. doi: 10.1038/s41540-020-00168-0
|
| [5] |
P. Paci, G. Fiscon, F. Conte, V. Licursi, J. Morrow, C. Hersh, et al., Integrated transcriptomic correlation network analysis identifies COPD molecular determinants, Sci. Rep., 10 (2020), 3361. doi: 10.1038/s41598-020-60228-7
|
| [6] |
G. Fiscon, F. Conte, V. Licursi, S. Nasi, P. Paci, Computational identification of specific genes for glioblastoma stem-like cells identity, Sci. Rep., 8 (2018), 7769. doi: 10.1038/s41598-018-26081-5
|
| [7] |
N. T. Doncheva, T. Kacprowski, M. Albrecht, Recent approaches to the prioritization of candidate disease genes, Wiley Interdiscip. Rev. Syst. Biol. Med., 4 (2012), 429-442. doi: 10.1002/wsbm.1177
|
| [8] |
A. Suratanee, K. Plaimas, Identification of inflammatory bowel disease-related proteins using a reverse k-nearest neighbor search, J. Bioinform. Comput. Biol., 12 (2014), 1450017. doi: 10.1142/S0219720014500176
|
| [9] | W. Guo, D.-M. Shang, J.-H. Cao, K. Feng, Y.-C. He, Y. Jiang, et al., Identifying and analyzing novel epilepsy-related genes using random walk with restart algorithm, Biomed. Res. Int., 2017 (2017), 1-14. |
| [10] |
S. D. Ghiassian, J. Menche, A.-L. Barabási, A DIseAse MOdule Detection (DIAMOnD) algorithm derived from a systematic analysis of connectivity patterns of disease proteins in the human interactome, PLoS Comput. Biol., 11 (2015), e1004120. doi: 10.1371/journal.pcbi.1004120
|
| [11] |
P. Paci, T. Colombo, G. Fiscon, A. Gurtner, G. Pavesi, L. Farina, SWIM: A computational tool to unveiling crucial nodes in complex biological networks, Sci. Rep., 7 (2017), 44797. doi: 10.1038/srep44797
|
| [12] |
S. Picart-Armada, S. J. Barrett, D. R. Wille, A. Perera-Lluna, A. Gutteridge, B. H. Dessailly, Benchmarking network propagation methods for disease gene identification, PLoS Comput. Biol., 15 (2019), e1007276. doi: 10.1371/journal.pcbi.1007276
|
| [13] |
D. Lancour, A. Naj, R. Mayeux, J. L. Haines, M. A. Pericak-Vance, G. D. Schellenberg, et al., One for all and all for one: Improving replication of genetic studies through network diffusion, PLoS Genet., 14 (2018), e1007306. doi: 10.1371/journal.pgen.1007306
|
| [14] |
S. Picart-Armada, W. K. Thompson, A. Buil, A. Perera-Lluna, diffuStats: An R package to compute diffusion-based scores on biological networks, Bioinformatics, 34 (2018), 533-534. doi: 10.1093/bioinformatics/btx632
|
| [15] |
E. Mosca, M. Bersanelli, M. Gnocchi, M. Moscatelli, G. Castellani, L. Milanesi, et al., Network diffusion-based prioritization of autism risk genes identifies significantly connected gene modules, Front. Genet., 8 (2017), 129. doi: 10.3389/fgene.2017.00129
|
| [16] |
M. Bersanelli, E. Mosca, D. Remondini, G. Castellani, L. Milanesi, Network diffusion-based analysis of high-throughput data for the detection of differentially enriched modules, Sci. Rep., 6 (2016), 34841. doi: 10.1038/srep34841
|
| [17] |
A. Hill, S. Gleim, F. Kiefer, F. Sigoillot, J. Loureiro, J. Jenkins, et al., Benchmarking network algorithms for contextualizing genes of interest, PLoS Comput. Biol., 15 (2019), e1007403. doi: 10.1371/journal.pcbi.1007403
|
| [18] |
A. Al-Aamri, K. Taha, Y. Al-Hammadi, M. Maalouf, D. Homouz, Analyzing a co-occurrence gene-interaction network to identify disease-gene association, BMC Bioinform., 20 (2019), 70. doi: 10.1186/s12859-019-2634-7
|
| [19] |
X. Zhao, Z.-P. Liu, Analysis of topological parameters of complex disease genes reveals the importance of location in a biomolecular network, Genes, 10 (2019), 143. doi: 10.3390/genes10020143
|
| [20] | S. Izudheen, E. S. Sajan, I. George, J. John, C. S. Attipetty, Effect of community structures in protein--protein interaction network in cancer protein identification, Curr. Sci., 118 (2020), 62. |
| [21] |
D. Szklarczyk, A. L. Gable, D. Lyon, A. Junge, S. Wyder, J. Huerta-Cepas, et al., STRING v11: Protein--protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets, Nucleic Acids Res., 47 (2019), D607-D613. doi: 10.1093/nar/gky1131
|
| [22] | J. Pinero, N. Queralt-Rosinach, A. Bravo, J. Deu-Pons, A. Bauer-Mehren, M. Baron, et al., DisGeNET: A discovery platform for the dynamical exploration of human diseases and their genes. Database: the journal of biological databases and curation. 2015; 2015: bav028: Epub 2015/04/17. doi: 10.1093/database/bav028.PubMedPMID:25877637. |
| [23] | A. J. Smola, R. Kondor, Kernels and regularization on graphs, Learn. theory kernel Mach., Springer, (2003), 144-158. |
| [24] | P. Y. Chebotarev, E. Shamis, The matrix-forest theorem and measuring relations in small social Groups, arXiv, 58 (1997), 1505-1514. |
| [25] |
A.-L. Barabási, R. Albert, Emergence of scaling in random networks, Science, 286 (1999), 509. doi: 10.1126/science.286.5439.509
|
| [26] | L. Page, S. Brin, R. Motwani, T. Winograd, The PageRank citation ranking: Bringing order to the web, 1999, 1-17. |
| [27] | S. Ballouz, M. Weber, P. Pavlidis, J. Gillis, EGAD: Ultra-fast functional analysis of gene networks, Bioinformatics, 33 (2017), 612-614. |
| [28] |
G. Valentini, G. Armano, M. Frasca, J. Lin, M. Mesiti, M. Re, RANKS: A flexible tool for node label ranking and classification in biological networks, Bioinformatics, 32(2016), 2872-2874. doi: 10.1093/bioinformatics/btw235
|
| [29] | S. Mostafavi, D. Ray, D. Warde-Farley, C. Grouios, Q. Morris, GeneMANIA: A real-time multiple association network integration algorithm for predicting gene function, Genome Biol., 9 (2008), S4. |
| [30] |
S. Picart-Armada, F. Fernández-Albert, M. Vinaixa, M. A. Rodríguez, S. Aivio, T. H. Stracker, et al., Null diffusion-based enrichment for metabolomics data, PLoS One, 12 (2017), e0189012. doi: 10.1371/journal.pone.0177197
|
| [31] |
B. Jiang, K. Kloster, D. F. Gleich, M. Gribskov, AptRank: An adaptive PageRank model for protein function prediction on bi-relational graphs, Bioinformatics, 33 (2017), 1829-1836. doi: 10.1093/bioinformatics/btx029
|
| [32] |
Y. Zhang, R.-q. He, Y.-w. Dang, X.-l. Zhang, X. Wang, S.-n. Huang, et al., Comprehensive analysis of the long noncoding RNA HOXA11-AS gene interaction regulatory network in NSCLC cells, Cancer Cell Int., 16 (2016), 89. doi: 10.1186/s12935-016-0366-6
|
| [33] | K. Ge, J. Huang, W. Wang, M. Gu, X. Dai, Y. Xu, et al., Serine protease inhibitor kazal-type 6 inhibits tumorigenesis of human hepatocellular carcinoma cells via its extracellular action, Oncotarget, 8 (2016), 5965-5975. |
| [34] | J. Li, X. Wang, J. Yang, S. Zhao, T. Liu, L. Wang, Identification of hub genes in Hepatocellular Carcinoma related to progression and prognosis by weighted gene co-expression network analysis, Med. Sci. Monit, 26 (2020), e920854. |
| [35] |
Y. J. Sung, T.W. Winkler, L. de Las Fuentes, A. R. Bentley, M. R. Brown, A. T. Kraja, et al., A large-scale multi-ancestry genome-wide study accounting for smoking behavior identifies multiple significant loci for blood pressure, Am. J. Hum. Genet., 102 (2018), 375-400. doi: 10.1016/j.ajhg.2018.01.015
|
| [36] |
S. Pasquin, M. Sharma, J.-F. Gauchat, Ciliary neurotrophic factor (CNTF): New facets of an old molecule for treating neurodegenerative and metabolic syndrome pathologies, Cytokine Growth Factor Rev., 26 (2015), 507-515. doi: 10.1016/j.cytogfr.2015.07.007
|
| [37] |
C. Conejero-Goldberg, T. M. Hyde, S. Chen, U. Dreses-Werringloer, M. M. Herman, J. E. Kleinman, et al., Molecular signatures in post-mortem brain tissue of younger individuals at high risk for Alzheimer's disease as based on APOE genotype, Mol. Psychiatry, 16 (2011), 836-847. doi: 10.1038/mp.2010.57
|
| [38] |
Y. Hashimoto, M. Kurita, M. Matsuoka, Identification of soluble WSX-1 not as a dominant-negative but as an alternative functional subunit of a receptor for an anti-Alzheimer's disease rescue factor Humanin, Biochem. Biophys. Res. Commun., 389 (2009), 95-99. doi: 10.1016/j.bbrc.2009.08.095
|
| [39] |
Y. Hashimoto, M. Kurita, S. Aiso, I. Nishimoto, M. Matsuoka, Humanin inhibits neuronal cell death by interacting with a cytokine receptor complex or complexes Involving CNTF Receptor/WSX-1/gp130, Mol. Biol. Cell, 20 (2009), 2864-2873. doi: 10.1091/mbc.e09-02-0168
|
 mbe-18-03-147_supplementary_file.docx |
 |
| 1. | Ruiwen WU, Xiuxiang LIU, Dynamics of a predator-prey system with a mate-finding Allee effect on prey, 2017, 41, 13000098, 585, 10.3906/mat-1411-8 | |
| 2. | Ruiwen Wu, Xiuxiang Liu, Dynamics of a Predator-Prey System with a Mate-Finding Allee Effect, 2014, 2014, 1085-3375, 1, 10.1155/2014/673424 | |
| 3. | J. Leonel Rocha, Abdel-Kaddous Taha, D. Fournier-Prunaret, Allee’s dynamics and bifurcation structures in von Bertalanffy’s population size functions, 2018, 990, 1742-6588, 012011, 10.1088/1742-6596/990/1/012011 | |
| 4. | Pablo Aguirre, José D. Flores, Eduardo González-Olivares, Bifurcations and global dynamics in a predator–prey model with a strong Allee effect on the prey, and a ratio-dependent functional response, 2014, 16, 14681218, 235, 10.1016/j.nonrwa.2013.10.002 | |
| 5. | Nicole Martínez-Jeraldo, Pablo Aguirre, Allee effect acting on the prey species in a Leslie–Gower predation model, 2019, 45, 14681218, 895, 10.1016/j.nonrwa.2018.08.009 | |
| 6. | J. Leonel Rocha, Abdel-Kaddous Taha, Danièle Fournier-Prunaret, Big bang bifurcations in von Bertalanffy’s dynamics with strong and weak Allee effects, 2016, 84, 0924-090X, 607, 10.1007/s11071-015-2510-6 | |
| 7. | Viviana Rivera, Pablo Aguirre, Study of a Tritrophic Food Chain Model with Non-differentiable Functional Response, 2020, 165, 0167-8019, 19, 10.1007/s10440-019-00239-3 | |
| 8. | J. Leonel Rocha, Danièle Fournier-Prunaret, Abdel-Kaddous Taha, Strong and weak Allee effects and chaotic dynamics in Richards' growths, 2013, 18, 1553-524X, 2397, 10.3934/dcdsb.2013.18.2397 | |
| 9. | Pablo Aguirre, A general class of predation models with multiplicative Allee effect, 2014, 78, 0924-090X, 629, 10.1007/s11071-014-1465-3 | |
| 10. | Alessandro Arsie, Chanaka Kottegoda, Chunhua Shan, High Codimension Bifurcations of a Predator–Prey System with Generalized Holling Type III Functional Response and Allee Effects, 2022, 1040-7294, 10.1007/s10884-022-10214-6 | |
| 11. | Feifan Zhang, Hao Tian, Hongfan Zhao, Xinran Zhang, Qiyu Shi, Spatiotemporal Pattern Formation in a Discrete Toxic-Phytoplankton–Zooplankton Model with Cross-Diffusion and Weak Allee Effect, 2022, 32, 0218-1274, 10.1142/S0218127422501565 | |
| 12. | Alessandro Arsie, Chanaka Kottegoda, Chunhua Shan, A predator-prey system with generalized Holling type IV functional response and Allee effects in prey, 2022, 309, 00220396, 704, 10.1016/j.jde.2021.11.041 | |
| 13. | Uttam Ghosh, Susmita Sarkar, Prabir Chakraborty, Stability and Bifurcation Analysis of a Discrete Prey-Predator Model with Mate-Finding Allee, Holling Type-I Functional Response and Predator Harvesting, 2022, 52, 0103-9733, 10.1007/s13538-022-01189-2 | |
| 14. | Arturo Mena-Lorca, Jaime Mena-Lorca, Astrid Morales-Soto, 2022, Chapter 16, 978-3-031-04270-6, 347, 10.1007/978-3-031-04271-3_16 | |
| 15. | Henan Wang, Ping Liu, Pattern dynamics of a predator–prey system with cross-diffusion, Allee effect and generalized Holling IV functional response, 2023, 171, 09600779, 113456, 10.1016/j.chaos.2023.113456 | |
| 16. | Zuchong Shang, Yuanhua Qiao, Bifurcation analysis in a predator–prey model with strong Allee effect on prey and density‐dependent mortality of predator, 2023, 0170-4214, 10.1002/mma.9793 | |
| 17. | Manoj Kumar Singh, Arushi Sharma, Luis M. Sánchez-Ruiz, Impact of the Allee Effect on the Dynamics of a Predator–Prey Model Exhibiting Group Defense, 2025, 13, 2227-7390, 633, 10.3390/math13040633 |
Figures(9) / Tables(5)
Panisa Janyasupab, Apichat Suratanee, Kitiporn Plaimas. Network diffusion with centrality measures to identify disease-related genes[J]. Mathematical Biosciences and Engineering, 2021, 18(3): 2909-2929. doi: 10.3934/mbe.2021147
DownLoad: