Network-based ranking methods for prediction of novel disease associated microRNAs

Computational Biology and Chemistry

Volumn 58, Issue , 2015, Pages 139-148

  Le, Duc Hau ^a  

^a Water Resources University   (Viet Nam)

Author keywords

Breast cancer; Network based ranking algorithm; Prediction of novel disease associated microRNAs

Indexed keywords

ACTIVE NETWORKS; ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPLEX NETWORKS; DISEASES; FORECASTING; GENES; LEARNING SYSTEMS; PROTEINS; SOCIAL NETWORKING (ONLINE); TOPOLOGY;

BREAST CANCER; DISEASE GENE PREDICTIONS; FUNCTIONAL SIMILARITY; MICRORNAS; PROTEIN INTERACTION NETWORKS; RANDOM WALK WITH RESTART; RANKING ALGORITHM; TOPOLOGICAL PROPERTIES;

RNA;

MICRORNA;

ALGORITHM; BIOLOGY; BREAST TUMOR; DISEASES; GENE REGULATORY NETWORK; GENETICS; HUMAN;

ALGORITHMS; BREAST NEOPLASMS; COMPUTATIONAL BIOLOGY; DISEASE; GENE REGULATORY NETWORKS; HUMANS; MICRORNAS;

EID: 84938372850     PISSN: 14769271     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compbiolchem.2015.07.003     Document Type: Article

References (67)

1. A.S. Ali, S. Ali, A. Ahmad, B. Bao, P.A. Philip, and F.H. Sarkar Expression of microRNAs: potential molecular link between obesity, diabetes and cancer Obes. Rev. 12 2011 1050 1062
2. Y. Assenov, F. Ramírez, S.-E. Schelhorn, T. Lengauer, and M. Albrecht Computing topological parameters of biological networks Bioinformatics 24 2008 282 284
3. D.P. Bartel MicroRNAs: genomics, biogenesis, mechanism, and function Cell 116 2004 281 297
4. I. Bentwich, A. Avniel, Y. Karov, R. Aharonov, S. Gilad, O. Barad, A. Barzilai, P. Einat, U. Einav, and E. Meiri Identification of hundreds of conserved and nonconserved human microRNAs Nat. Genet. 37 2005 766 770
5. M.K. Bhayani, G.A. Calin, and S.Y. Lai Functional relevance of miRNA∗sequences in human disease Mutat. Res. Fundam. Mol. Mech. Mutagen. 731 2012 14 19
6. K. Chen, and N. Rajewsky The evolution of gene regulation by transcription factors and microRNAs Nat. Rev. Genet. 8 2007 93 103
7. H. Chen, and Z. Zhang Prediction of associations between OMIM diseases and microRNAs by random walk on OMIM disease similarity network Sci. World J. 2013 2013 6
8. J. Chen, B. Aronow, and A. Jegga Disease candidate gene identification and prioritization using protein interaction networks BMC Bioinform. 10 2009 73
9. X. Chen, M.-X. Liu, Q.-H. Cui, and G.-Y. Yan Prediction of disease-related interactions between microRNAs and environmental factors based on a semi-supervised classifier PLoS One 7 2012 e43425
10. X. Chen, M.-X. Liu, and G.-Y. Yan RWRMDA: predicting novel human microRNA-disease associations Mol. BioSyst. 8 2012 2792 2798
11. R. Conrad, M. Barrier, and L.P. Ford Role of miRNA and miRNA processing factors in development and disease Birth Defects Res. Part C: Embryo Today: Rev. 78 2006 107 117
12. B.L. Davidson, and P.B. McCray Current prospects for RNA interference-based therapies Nat. Rev. Genet. 12 2011 329 340
13. N.T. Doncheva, T. Kacprowski, and M. Albrecht Recent approaches to the prioritization of candidate disease genes Wiley Interdiscip. Rev.: Syst. Biol. Med. 4 2012 429 442
14. M. Esteller Non-coding RNAs in human disease Nat. Rev. Genet. 12 2011 861 874
15. J.-B. Fan, W. Gommans, and E. Berezikov Controlling miRNA regulation in disease Next-Generation MicroRNA Expression Profiling Technology vol. 822 2012 Humana Press 1 18 Methods in Molecular Biology
16. I. Feldman, A. Rzhetsky, and D. Vitkup Network properties of genes harboring inherited disease mutations Proc. Natl. Acad. Sci. 105 2008 4323 4328
17. R.C. Friedman, K.K.-H. Farh, C.B. Burge, and D.P. Bartel Most mammalian mRNAs are conserved targets of microRNAs Genome Res. 19 2009 92 105
18. K.-I. Goh, M.E. Cusick, D. Valle, B. Childs, M. Vidal, and A.-L. Barabási The human disease network Proc. Natl. Acad. Sci. 104 2007 8685 8690
19. L. He, and G.J. Hannon MicroRNAs: small RNAs with a big role in gene regulation Nat. Rev. Genet. 5 2004 522 531
20. Q. Jiang, Y. Wang, Y. Hao, L. Juan, M. Teng, X. Zhang, M. Li, G. Wang, and Y. Liu miR2Disease: a manually curated database for microRNA deregulation in human disease Nucleic Acids Res. 37 2009 D98 104
21. Q. Jiang, G. Wang, and Y. Wang An approach for prioritizing disease-related microRNAs based on genomic data integration Biomedical Engineering and Informatics (BMEI), 2010 3rd International Conference IEEE 2010 2270 2274
22. Q. Jiang, Y. Hao, G. Wang, L. Juan, T. Zhang, M. Teng, Y. Liu, and Y. Wang Prioritization of disease microRNAs through a human phenome-microRNAome network BMC Syst. Biol. 4 2010 S2
23. D. Jones Steps on the road to personalized medicine Nat. Rev. Drug Discov. 6 2007 770 771
24. M.G. Kann Advances in translational bioinformatics: computational approaches for the hunting of disease genes Brief. Bioinform. 11 2009 96 110
25. M.A. Kay State-of-the-art gene-based therapies: the road ahead Nat. Rev. Genet. 12 2011 316 328
26. M. Kertesz, N. Iovino, U. Unnerstall, U. Gaul, and E. Segal The role of site accessibility in microRNA target recognition Nat. Genet. 39 2007 1278 1284
27. S. Kohler, S. Bauer, D. Horn, and P. Robinson Walking the interactome for prioritization of candidate disease genes Am. J. Hum. Genet. 82 2008 949 958
28. A. Kozomara, and S. Griffiths-Jones miRBase: annotating high confidence microRNAs using deep sequencing data Nucleic Acids Res. 42 2014 D68 D73
29. D.-H. Le, and Y.-K. Kwon GPEC: a Cytoscape plug-in for random walk-based gene prioritization and biomedical evidence collection Comput. Biol. Chem. 37 2012 17 23
30. D.-H. Le, and Y.-K. Kwon Neighbor-favoring weight reinforcement to improve random walk-based disease gene prioritization Comput. Biol. Chem. 44 2013 1 8
31. D.-H. Le, and K. Marchal Integration of miRNA-miRNA networks improves the prediction of novel disease associated miRNAs The First NAFOSTED Conference on Information and Computer Science 2014 438 448
32. B.P. Lewis, I.H. Shih, M.W. Jones-Rhoades, D.P. Bartel, and C.B. Burge Prediction of mammalian microRNA targets Cell 115 2003 787 798
33. B.P. Lewis, C.B. Burge, and D.P. Bartel Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets Cell 120 2005 15 20
34. Y. Li, and K.V. Kowdley MicroRNAs in common human diseases Genomics Proteomics Bioinform. 10 2012 246 253
35. Y. Li, and J.C. Patra Genome-wide inferring gene-phenotype relationship by walking on the heterogeneous network Bioinformatics 26 2010 1219 1224
36. Y. Li, C. Qiu, J. Tu, B. Geng, J. Yang, T. Jiang, and Q. Cui HMDD v2.0: a database for experimentally supported human microRNA and disease associations Nucleic Acids Res. 42 2014 D1070 D1074
37. L.P. Lim, N.C. Lau, E.G. Weinstein, A. Abdelhakim, S. Yekta, M.W. Rhoades, C.B. Burge, and D.P. Bartel The microRNAs of Caenorhabditis elegans Genes Dev. 17 2003 991 1008
38. X. Liu, L. Zou, L. Zhu, H. Zhang, C. Du, Z. Li, C. Gao, X. Zhao, S. Bao, and H. Zheng miRNA mediated up-regulation of cochaperone p23 acts as an anti-apoptotic factor in childhood acute lymphoblastic leukemia Leuk. Res. 36 2012 1098 1104
39. M. Lu, Q. Zhang, M. Deng, J. Miao, Y. Guo, W. Gao, and Q. Cui An analysis of human microRNA and disease associations PLoS One 3 2008 e3420
40. K. Lundstrom Micro-RNA in disease and gene therapy Curr. Drug Discov. Technol. 8 2011 76 86
41. A. Masoudi-Nejad, A. Meshkin, B. Haji-Eghrari, and G. Bidkhori Candidate gene prioritization Mol. Genet. Genomics 287 2012 679 698
42. I. Melnikova RNA-based therapies Nat. Rev. Drug Discov. 6 2007 863 864
43. J.T. Mendell, and E.N. Olson MicroRNAs in stress signaling and human disease Cell 148 2012 1172 1187
44. Y. Moreau, and L.-C. Tranchevent Computational tools for prioritizing candidate genes: boosting disease gene discovery Nat. Rev. Genet. 13 2012 523 536
45. J. Morrison, R. Breitling, D. Higham, and D. Gilbert GeneRank: using search engine technology for the analysis of microarray experiments BMC Bioinform. 6 2005 233
46. R. Murria Estal, S. Palanca Suela, I. de Juan Jiménez, C. Egoavil Rojas, Z. García-Casado, M. Juan Fita, A. Sánchez Heras, Á. Segura Huerta, I. Chirivella González, D. Sánchez-Izquierdo, and et al. MicroRNA signatures in hereditary breast cancer Breast Cancer Res. Treat. 142 2013 19 30
47. S. Navlakha, and C. Kingsford The power of protein interaction networks for associating genes with diseases Bioinformatics 26 2010 1057 1063
48. M. Oti, and H.G. Brunner The modular nature of genetic diseases Clin. Genet. 71 2007 1 11
49. Page, L., Brin, S., Motwani, R., Winograd, T., 1999. The PageRank Citation Ranking: Bringing Order to the Web.
50. A. Qabaja, M. Alshalalfa, T. Bismar, and R. Alhajj Protein network-based Lasso regression model for the construction of disease-miRNA functional interactions EURASIP J. Bioinform. Syst. Biol. 2013 2013 3
51. J. Qinghua, W. Guohua, Z. Tianjiao, and W. Yadong Predicting human microRNA-disease associations based on support vector machine Bioinformatics and Biomedicine (BIBM), 2010 IEEE International Conference 18-21 December 2010 467 472
52. J. Qinghua, H. Yangyang, W. Guohua, Z. Tianjiao, and W. Yadong Weighted network-based inference of human microRNA-disease associations Frontier of Computer Science and Technology (FCST), 2010 Fifth International Conference 18-22 August 2010 431 435
53. C. Qiu, G. Chen, and Q. Cui Towards the understanding of microRNA and environmental factor interactions and their relationships to human diseases Sci. Rep. 2 2012
54. J. Rosa-Rosa, G. Pita, A. González-Neira, R. Milne, V. Fernandez, C. Ruivenkamp, C. van Asperen, P. Devilee, and J. Benitez A 7 Mb region within 11q13 may contain a high penetrance gene for breast cancer Breast Cancer Res. Treat. 118 2009 151 159
55. V. Rottiers, and A.M. Näär MicroRNAs in metabolism and metabolic disorders Nat. Rev. Mol. Cell Biol. 13 2012 239 250
56. M. Salvatore, A. Magrelli, and D. Taruscio The role of microRNAs in the biology of rare diseases Int. J. Mol. Sci. 12 2011 6733 6742
57. M.E. Smoot, K. Ono, J. Ruscheinski, P.-L. Wang, and T. Ideker Cytoscape 2.8: new features for data integration and network visualization Bioinformatics 27 2011 431 432
58. I. Steinfeld, R. Navon, R. Ach, and Z. Yakhini miRNA target enrichment analysis reveals directly active miRNAs in health and disease Nucleic Acids Res. 41 2013 e45
59. L.-C. Tranchevent, F.B. Capdevila, D. Nitsch, B. De Moor, P. De Causmaecker, and Y. Moreau A guide to web tools to prioritize candidate genes Brief. Bioinform. 12 2010 22 32
60. E. van Rooij, W.S. Marshall, and E.N. Olson Toward microRNA-based therapeutics for heart disease: the sense in antisense Circ. Res. 103 2008 919 928
61. O. Vanunu, O. Magger, E. Ruppin, T. Shlomi, and R. Sharan Associating genes and protein complexes with disease via network propagation PLoS Comput. Biol. 6 2010 e1000641
62. S. Volinia, and C.M. Croce Prognostic microRNA/mRNA signature from the integrated analysis of patients with invasive breast cancer Proc. Natl. Acad. Sci. 110 2013 7413 7417
63. D. Wang, J. Wang, M. Lu, F. Song, and Q. Cui Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases Bioinformatics 26 2010 1644 1650
64. S. White, and P. Smyth Algorithms for estimating relative importance in networks Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining ACM 2003 266 275
65. J. Xu, C.-X. Li, J.-Y. Lv, Y.-S. Li, Y. Xiao, T.-T. Shao, X. Huo, X. Li, Y. Zou, Q.-L. Han, and et al. Prioritizing candidate disease miRNAs by topological features in the miRNA target-dysregulated network: case study of prostate cancer Mol. Cancer Ther. 10 2011 1857 1866
66. J. Xu, C.-X. Li, Y.-S. Li, J.-Y. Lv, Y. Ma, T.-T. Shao, L.-D. Xu, Y.-Y. Wang, L. Du, Y.-P. Zhang, and et al. MiRNA-miRNA synergistic network: construction via co-regulating functional modules and disease miRNA topological features Nucleic Acids Res. 39 2011 825 836
67. L.-X. Yan, X.-F. Huang, Q. Shao, M.A.Y. Huang, L. Deng, Q.-L. Wu, Y.-X. Zeng, and J.-Y. Shao MicroRNA miR-21 overexpression in human breast cancer is associated with advanced clinical stage, lymph node metastasis and patient poor prognosis RNA 14 2008 2348 2360