Biometrics and Bioinformatics

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miRNA-miRNA and Disease-Disease Similarity Identification and Disease Detection Using KBNSI


Affiliations
1 Department of Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, India
     

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Increasing evidences have indicated that microRNAs (miRNAs) are functionally associated with the development and progression of various complex human diseases. However, the roles of miRNAs in multiple biological processes or various diseases and their underlying molecular mechanisms still have not been fully understood yet. Meanwhile, target diseases need to be revealed for some new microRNAs without any known target disease association information as new microRNAs are discovered each year. Therefore, computational methods for microRNA-disease association prediction have gained a lot of research interest. Considering the limitations in previous computational methods, here developing the model of Kernel Based Network Similarity Integration (KBNSI). It integrate the result of both the miRNA-miRNA association and the disease disease association similarity and then it will integrates the both results, then detect the disease.

Keywords

Measuring the Similarity, miRNA and Disease Association, Disease-Related miRNAs, Disease-Disease Association, miRNA Associations, miRNA Similarity, Disease Similarity, Similarity Matrix.
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  • P. Jimenez, F. Thomas, and C. Torras, “3D collision detection: A survey,” Computers & Graphics, vol. 25, no. 2, pp. 269-285, 2001. View at Publisher · View at Google Scholar. View at Scopus.

  • T. Uziel, F. V. Karginov, S. Xie et al., “The miR-17∼92 cluster collaborates with the Sonic Hedgehog pathway in medulloblastoma,” Proceedings of the National Academy of Sciences of the United States of America, vol. 106, no. 8, pp. 2812-2817, 2009. View at Publisher · View at Google Scholar.

  • H. Ding, P. Feng, W. Chen, and H. Lin, “Identification of bacteriophage virion proteins by the ANOVA feature selection and analysis,” Molecular BioSystems, vol. 10, no. 8, pp. 2229-2235, 2014. View at Publisher · View at Google Scholar.

  • 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, vol. 26, no. 13, pp. 1644-1650, 2010. View at Publisher · View at Google Scholar · View at Scopus.

  • Y. Wang, T. Cui, C. Zhang et al., “Global protein-protein interaction network in the human pathogen mycobacterium tuberculosis H37Rv,” Journal of Proteome Research, vol. 9, no. 12, pp. 6665-6677, 2010. View at Publisher · View at Google Scholar. View at Scopus.

  • Y. Wang, L. Chen, B. Chen et al., “Mammalian ncRNA-disease repository: a global view of ncRNA-mediated disease network,” Cell Death & Disease, vol. 4, article e765, 2013. View at Publisher · View at Google Scholar. View at Scopus.

  • A. Esquela-Kerscher and F. J. Slack, “Oncomirs-microRNAs with a role in cancer,” Nature Reviews Cancer, vol. 6, no. 4, pp. 259-269, 2006. View at Publisher · View at Google Scholar · View at Scopus.

  • M. V. G. Latronico, D. Catalucci, and G. Condorelli, “Emerging role of microRNAs in cardiovascular biology,” Circulation Research, vol. 101, no. 12, pp. 1225-1236, 2007. View at Publisher · View at Google Scholar · View at Scopus.

  • M. Lu, Q. Zhang, M. Deng et al., “An analysis of human microRNA and disease associations,” PLoS ONE, vol. 3, no. 10, Article ID e3420, 2008. View at Publisher · View at Google Scholar. View at Scopus.

  • G. A. Calin and C. M. Croce, “MicroRNA signatures in human cancers,” Nature Reviews Cancer, vol. 6, no. 11, pp. 857-866, 2006. View at Publisher · View at Google Scholar. View at Scopus.

  • M. V. Iorio, M. Ferracin, C.-G. Liu et al., “MicroRNA gene expression deregulation in human breast cancer,” Cancer Research, vol. 65, no. 16, pp. 7065-7070, 2005. View at Publisher · View at Google Scholar. View at Scopus.

  • A. Esquela-Kerscher, P. Trang, J. F. Wiggins et al., “The let-7 microRNA reduces tumor growth in mouse models of lung cancer,” Cell Cycle, vol. 7, no. 6, pp. 759-764, 2008. View at Publisher · View at Google Scholar. View at Scopus.

  • Q. Wang, L. Wei, X. Guan, Y. Wu, Q. Zou, and Z. Ji, “Briefing in family characteristics of microRNAs and their applications in cancer research,” Biochimica et Biophysica Acta-Proteins and Proteomics, vol. 1844, no. 1, pp. 191-197, 2014. View at Publisher · View at Google Scholar. View at Scopus.

  • B. Yang, H. Lin, J. Xiao et al., “The muscle-specific microRNA miR-1 regulates cardiac arrhythmogenic potential by targeting GJA1 and KCNJ2,” Nature Medicine, vol. 13, no. 4, pp. 486-491, 2007. View at Publisher · View at Google Scholar. View at Scopus.

  • R. W. Chen, L. T. Bemis, C. M. Amato et al., “Truncation in CCND1 mRNA alters miR-16-1 regulation in mantle cell lymphoma,” Blood, vol. 112, no. 3, pp. 822-829, 2008. View at Publisher · View at Google Scholar. View at Scopus.

  • J. Xu, C.-X. Li, Y.-S. Li et al., “MiRNA-miRNA synergistic network: construction via co-regulating functional modules and disease miRNA topological features,” Nucleic Acids Research, vol. 39, no. 3, pp. 825-836, 2011. View at Publisher · View at Google Scholar. View at Scopus.

  • T.-H. Cheung, K.-N. M. Man, M.-Y. Yu et al., “Dysregulated microRNAs in the pathogenesis and progression of cervical neoplasm,” Cell Cycle, vol. 11, no. 15, pp. 2876-2884, 2012. View at Publisher · View at Google Scholar. View at Scopus.

  • X. Zeng, X. Zhang and Q. Zou, Briefings Bioinf., 2016, 17, 193-203.

  • Q. H. Jiang, Y. Y. Hao, G. H. Wang, L. R. Juan, T. J. Zhang, M. X. Teng, Y. L. Liu and Y. D. Wang, BMC Syst. Biol., 2010.

  • D. Wang, J. A. Wang, M. Lu, F. Song and Q. H. Cui, Bioinformatics, 2010, 26, 1644-1650.

  • X. Chen, M. X. Liu and G. Y. Yan, Mol. BioSyst., 2012, 8 [22] H. Shi, J. Xu, G. Zhang, L. Xu, C. Li, L. Wang, Z. Zhao, W. Jiang, Z. Guo and X. Li, BMC Syst. Biol., 2013, 7, 1-12.

  • P. Xuan, K. Han, M. Guo, Y. Guo, J. Li, J. Ding, Y. Liu, Q. Dai, J. Li, Z. Teng and Y. Huang, PLoS One, 2013, 8, e70204.

  • H. Shi, G. Zhang, M. Zhou, L. Cheng, H. Yang, J. Wang, J. Sun and Z. Wang, PLoS One, 2016, 11, e0148521.

  • X. Chen, C. C. Yan, X. Zhang, Z. H. You, L. X. Deng, Y. Liu, Y. D. Zhang and Q. H. Dai, Sci. Rep., 2016.

  • J. Luo, P. Ding, C. Liang, B. Cao and X. Chen, IEEE/ACM Trans. Comput. Biol. Bioinf., 2016.

  • Q. Zou, J. Li, Q. Hong, Z. Lin, Y. Wu, H. Shi and Y. Ju, BioMed Res. Int., 2015, 2015, 810514.

  • Q. Jiang, G. Wang, S. Jin, Y. Li and Y. Wang, Int. J. Data Min. Bioinform., 2013, 8, 282-293.

  • 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 On, IEEE, 2010, vol. 6, pp.

  • S. Bandyopadhyay, R. Mitra, U. Maulik and M. Q. Zhang, Silence, 2010.

  • A. Schlicker, T. Lengauer and M. Albrecht, Bioinformatics, 2010, 26.

  • J. Jiang, E. J. Lee, Y. Gusev and T. D. Schmittgen, Nucleic Acids Res., 2005.

  • K. Musilova and M. Mraz, Leukemia, 2015, 29, 1004-1017.

  • S. McGuire, Adv. Nutr., 2016, 7, 418-419.

  • P. D. Baade, D. R. Youlden and L. J. Krnjacki, Mol. Nutr. Food Res., 2009, 53.

  • M. Alshalalfa, G. D. Bader, A. Goldenberg, Q. Morris and R. Alhajj, BMC Syst. Biol., 2012.

  • M. Jin, T. Zhang, C. Liu, M. A. Badeaux, B. Liu, R. Liu, C. Jeter, X. Chen, A. V. Vlassov and D. G. Tang, Cancer Res., 2014.

  • M. Ammad-ud-din, E. Georgii, M. Gonen, T. Laitinen, O. Kallioniemi, K. Wennerberg, A. Poso and S. Kaski, “Integrative and personalized QSAR analysis in cancer by kernelized Bayesian matrix factorization,” J Chem Inf Model, vol. 54, no. 8, pp. 2347-59, 2014.

  • M. Gon en and S. Kaski, “Kernelized Bayesian Matrix Factorization,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 10, pp. 2047-2060, 2015.


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  • miRNA-miRNA and Disease-Disease Similarity Identification and Disease Detection Using KBNSI

Abstract Views: 229  |  PDF Views: 2

Authors

MP. Jijith
Department of Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, India
Joby George
Department of Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, India

Abstract


Increasing evidences have indicated that microRNAs (miRNAs) are functionally associated with the development and progression of various complex human diseases. However, the roles of miRNAs in multiple biological processes or various diseases and their underlying molecular mechanisms still have not been fully understood yet. Meanwhile, target diseases need to be revealed for some new microRNAs without any known target disease association information as new microRNAs are discovered each year. Therefore, computational methods for microRNA-disease association prediction have gained a lot of research interest. Considering the limitations in previous computational methods, here developing the model of Kernel Based Network Similarity Integration (KBNSI). It integrate the result of both the miRNA-miRNA association and the disease disease association similarity and then it will integrates the both results, then detect the disease.

Keywords


Measuring the Similarity, miRNA and Disease Association, Disease-Related miRNAs, Disease-Disease Association, miRNA Associations, miRNA Similarity, Disease Similarity, Similarity Matrix.

References