Computational Intelligence applied to Human Genome Data for the Dengue Severity Prognosis
Resumo
Dengue has become one of the most important worldwide arthropodborne diseases around the world. Here, one hundred and two Brazilian dengue virus (DENV) III patients and controls were genotyped for 322 innate immunity gene loci. All biological data (including age, sex and genome background) were analyzed using Machine Learning techniques to discriminate tendency to severe dengue phenotype development. Our current approach produces median values for accuracy greater than 86%, with sensitivity and specificity over 98% and 51%, respectively. Genome data information from 13 key immune polymorphic SNPs was used under different dominant or recessive models. Our approach is a valuable tool for early diagnosis of the severe form of dengue infection and can be used to identify individuals at high risk of developing this form of the disease even in uninfected individuals. The model also identifies various genes involved dengue severity.
Palavras-chave:
Biological Databases, Data Management, Data Integration, and Data Mining
Referências
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Jiang, X. and Braga-Neto, U. (2014). A naive-bayes approach to bolstered error estimation in high-dimensional spaces. In Signal and Information Processing (GlobalSIP), 2014 IEEE Global Conference on, pages 1398–1401. IEEE.
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Libraty, D. H., Endy, T. P., Houng, H.-S. H., Green, S., Kalayanarooj, S., Suntayakorn, S., Chansiriwongs, W., Vaughn, D. W., Nisalak, A., Ennis, F. A., et al. (2002). Differing influences of virus burden and immune activation on disease severity in secondary dengue-3 virus infections. The Journal of infectious diseases, 185(9):1213–1221.
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Nascimento, E. J., Silva, A. M., Cordeiro, M. T., Brito, C. A., Gil, L. H., Braga-Neto, U., and Marques, E. T. (2009). Alternative complement pathway deregulation is correlated with dengue severity. PloS one, 4(8):e6782.
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Soundravally, R. and Hoti, S. (2007). Immunopathogenesis of dengue hemorrhagic fever and shock syndrome: role of tap and hpa gene polymorphism. Human immunology, 68(12):973–979.
Tanner, L., Schreiber, M., Low, J. G., Ong, A., Tolfvenstam, T., Lai, Y. L., Ng, L. C., Leo,Y. S., Puong, L. T., Vasudevan, S. G., et al. (2008). Decision tree algorithms predict the diagnosis and outcome of dengue fever in the early phase of illness. PLoS neglected tropical diseases, 2(3):e196.
Wang, W.-K., Chao, D.-Y., Kao, C.-L., Wu, H.-C., Liu, Y.-C., Li, C.-M., Lin, S.-C., Ho, S.-T., Huang, J.-H., and King, C.-C. (2003). High levels of plasma dengue viral load during defervescence in patients with dengue hemorrhagic fever: implications for pathogenesis. Virology, 305(2):330–338.
World Health Organization, W., for Research, S. P., in Tropical Diseases, T., of Control of Neglected Tropical Diseases, W. H. O. D., Epidemic, W. H. O., and Alert, P. (2009). Dengue: guidelines for diagnosis, treatment, prevention and control. World Health Organization.
Ali, I., Humayun, F., Azam, S., Munir, A., Rizwan, M., et al. (2017). Computational tool for classification of dengue virus. J Appl Bioinforma Comput Biol 6, 3:2.
Bhatt, S., Gething, P. W., Brady, O. J., Messina, J. P., Farlow, A. W., Moyes, C. L., Drake, J. M., Brownstein, J. S., Hoen, A. G., Sankoh, O., et al. (2013). The global distribution and burden of dengue. Nature, 496(7446):504.
Braga-Neto, U. and Dougherty, E. (2004a). Bolstered error estimation. Pattern Recognition, 37(6):1267–1281.
Braga-Neto, U. M. and Dougherty, E. R. (2004b). Is cross-validation valid for smallsample microarray classification? Bioinformatics, 20(3):374–380.
Byrd, R. H., Lu, P., Nocedal, J., and Zhu, C. (1995). A limited memory algorithm for bound constrained optimization. SIAM Journal on Scientific Computing, 16(5):1190–1208.
Cordeiro, M. T., Braga-Neto, U., Nogueira, R. M. R., and Marques Jr, E. T. (2009). Reliable classifier to differentiate primary and secondary acute dengue infection based on igg elisa. PloS one, 4(4):e4945.
de Carvalho, C. X., Cardoso, C. C., Kehdy, F. d. S. G., Pacheco, A. G., and Moraes, M. O.(2017). Host genetics and dengue fever. Infection, Genetics and Evolution.
Fan, R.-E., Chang, K.-W., Hsieh, C.-J., Wang, X.-R., and Lin, C.-J. (2008). Liblinear: A library for large linear classification. Journal of machine learning research, 9(Aug):1871–1874.
Gomes, A. L. V., Wee, L. J., Khan, A. M., Gil, L. H., Marques Jr, E. T., Calzavara-Silva, C. E., and Tan, T. W. (2010). Classification of dengue fever patients based on gene expression data using support vector machines. PloS one, 5(6):e11267.
Guyon, I., Weston, J., Barnhill, S., and Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Machine learning, 46(1-3):389–422.
Jiang, X. and Braga-Neto, U. (2014). A naive-bayes approach to bolstered error estimation in high-dimensional spaces. In Signal and Information Processing (GlobalSIP), 2014 IEEE Global Conference on, pages 1398–1401. IEEE.
Keogh, E. and Mueen, A. (2011). Curse of dimensionality. In Encyclopedia of Machine Learning, pages 257–258. Springer.
Libraty, D. H., Endy, T. P., Houng, H.-S. H., Green, S., Kalayanarooj, S., Suntayakorn, S., Chansiriwongs, W., Vaughn, D. W., Nisalak, A., Ennis, F. A., et al. (2002). Differing influences of virus burden and immune activation on disease severity in secondary dengue-3 virus infections. The Journal of infectious diseases, 185(9):1213–1221.
McLachlan, G., Do, K.-A., and Ambroise, C. (2005). Analyzing microarray gene expression data, volume 422. John Wiley & Sons.
Muthusamy, K., Gopinath, K., and Nandhini, D. (2016). Computational prediction of immunodominant antigenic regions & potential protective epitopes for dengue vaccination. The Indian journal of medical research, 144(4):587.
Nascimento, E. J., Silva, A. M., Cordeiro, M. T., Brito, C. A., Gil, L. H., Braga-Neto, U., and Marques, E. T. (2009). Alternative complement pathway deregulation is correlated with dengue severity. PloS one, 4(8):e6782.
Paradoa, M. P., Trujillo, Y., and Basanta, P. (1987). Association of dengue hemorrhagic fever with the hla system. Haematologia, 20(2):83–87.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al. (2011). Scikit-learn: Machine learning in python. Journal of machine learning research, 12(Oct):2825–2830.
Sakuntabhai, A., Turbpaiboon, C., Casademont, I., Chuansumrit, A., Lowhnoo, T., Kajaste-Rudnitski, A., Kalayanarooj, S. M., Tangnararatchakit, K., Tangthawornchaikul, N., Vasanawathana, S., et al. (2005). A variant in the cd209 promoter is associated with severity of dengue disease. Nature genetics, 37(5):507.
Soundravally, R. and Hoti, S. (2007). Immunopathogenesis of dengue hemorrhagic fever and shock syndrome: role of tap and hpa gene polymorphism. Human immunology, 68(12):973–979.
Tanner, L., Schreiber, M., Low, J. G., Ong, A., Tolfvenstam, T., Lai, Y. L., Ng, L. C., Leo,Y. S., Puong, L. T., Vasudevan, S. G., et al. (2008). Decision tree algorithms predict the diagnosis and outcome of dengue fever in the early phase of illness. PLoS neglected tropical diseases, 2(3):e196.
Wang, W.-K., Chao, D.-Y., Kao, C.-L., Wu, H.-C., Liu, Y.-C., Li, C.-M., Lin, S.-C., Ho, S.-T., Huang, J.-H., and King, C.-C. (2003). High levels of plasma dengue viral load during defervescence in patients with dengue hemorrhagic fever: implications for pathogenesis. Virology, 305(2):330–338.
World Health Organization, W., for Research, S. P., in Tropical Diseases, T., of Control of Neglected Tropical Diseases, W. H. O. D., Epidemic, W. H. O., and Alert, P. (2009). Dengue: guidelines for diagnosis, treatment, prevention and control. World Health Organization.
Publicado
30/10/2018
Como Citar
DAVI, Caio et al.
Computational Intelligence applied to Human Genome Data for the Dengue Severity Prognosis. In: ARTIGOS CURTOS - SIMPÓSIO BRASILEIRO DE BIOINFORMÁTICA (BSB) , 2018, Niterói.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 25-30.
DOI: https://doi.org/10.5753/bsb_estendido.2018.8800.
