Abstract
Tuberculosis is a treatable but severe disease caused by Mycobacterium tuberculosis (Mtb). Recent statistics by international health organizations estimate the Mtb exposure to have reached over two billion individuals. Delay in disease diagnosis could be fatal, especially to the population at risk, such as individuals with compromised immune systems. Intelligent decision systems (IDS) provide a promising tool to expedite discovery of biomarkers, and to boost their impact on earlier prediction of the likelihood of the disease onset. A novel IDS (iTB) is designed that integrates results from molecular medicine and systems biology of Mtb infection to estimate model parameters for prediction of the dynamics of the gene networks in Mtb-infected laboratory animals. The mouse model identifies a number of genes whose expressions could be significantly altered during the TB activation. Among them, a much smaller number of the most informative genes for prediction of the onset of TB are selected using a modified version of Empirical Risk Minimization as in Vapnik’s statistical learning theory. A hybrid intelligent system is designed to take as input the mRNA abundance at a near genome-size from the individual-to-be-tested, measured 3-4 times. The algorithms determine if that individual is at risk of the onset of the disease based on our current analysis of mRNA data, and to predict the values of the biomarkers for a future period (of up to 60 days for mice; this may differ for humans). An early warning sign allows conducting gene expression analysis during the activation which aims to find key genes that are expressed. With rapid advances in low-cost genome-based diagnosis, this IDS architecture provides a promising platform to advance Personalized Health Care based on sequencing the genome and microarray analysis of samples obtained from individuals at risk. The novelty of the design of iTB lies in the integration of the IDS design principles and the solution of the biological problems hand-in-hand, so as to provide an AI framework for biologically better-targeted personalized prevention/treatment for the high-risk groups. The iTB design applies in more generality, and provides the potential for extension of our AI-approach to personalized-medicine to prevent other public health pandemics.
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World Health Organization: Global Tuberculosis Control: A short update to the, report (2009)
Volokhov, D.V., Chizhikov, V.E., Denkin, S., Zhang, Y.: Mycobacteria Protocols. Humana Press, New York (2008)
World Health Organization, http://www.who.int/topics/tuberculosis/en/
Murray, M.: Tuberculosis: The Essentials. Informa Healthcare (2010)
Hopewell, P.C.: Tuberculosis: The Essentials. Informa Healthcare (2010)
Triccas, J.A., Berthet, F.X., Pelicic, V., Gicquel, B.: Use of Fluorescence Induction and Sucrose Counter Selection to Identify Mycobacterium tuberculosis Genes Expressed Within Host Cells. Microbiology 145, 2923–2930 (1999)
Talaat, A.M., Lyons, R., Howard, S.T., Johnston, S.A.: The Temporal Expression Profile of Mycobacterium tuberculosis Infection in Mice. Proc. Natl. Acad. Sci., 4602–4607 (2004)
Wilson, M., DeRisi, J., Kristensen, H.H., Imboden, P., Rane, S., Brown, P.O., Schoolnik, G.K.: Exploring Drug-induced Alterations in Gene Expression in Mycobacterium tuberculosis by Microarray Hybridization. Proc. Natl. Acad. Sci., 12833–12838 (1999)
Behr, M.A., Wilson, M.A., Gill, W.P., Salamon, H., Schoolnik, G.K., Rane, S., Small, P.M.: Comparative Genomics of BCG Vaccines by Whole-Genome DNA Microarray. Science 284, 1520–1523 (1999)
Fisher, M.A., Plikaytis, B.B., Shinnick, T.M.: Microarray Analysis of the Mycobacterium tuberculosis Transcriptional Response to the Acidic Conditions Found in Phagosomes. J. Bacteriol. 184, 4025–4032 (2002)
Schneider, X.: Analysis of incomplete climate data: Estimation of mean values and covariance matrices and imputation of missing values. Journal of Climate 14, 853–871 (2001)
Fritsch, F.N., Carlson, R.E.: Monotone Piecewise Cubic Interpolation. SIAM J. Numerical Analysis 17, 238–246 (1980)
Poggio, T., Girosi, F.: Regularization algorithms for learning that are equivalent to multilayer networks. Science 247, 978–982 (1990)
Girosi, F.: An Equivalence Between Sparse Approximation and Support Vector Machines. Neural Computation 10, 1455–1480 (1998)
Smola, A.J., Schölkopf, B.: Form Regularization Operators to Support Vector Kernels. Morgan Kaufmann, San Francisco (1998)
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (2000)
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Ardalan, A., Selen, E.S., Dashti, H., Talaat, A., Assadi, A. (2011). Design and Applications of Intelligent Systems in Identifying Future Occurrence of Tuberculosis Infection in Population at Risk. In: Camarinha-Matos, L.M. (eds) Technological Innovation for Sustainability. DoCEIS 2011. IFIP Advances in Information and Communication Technology, vol 349. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19170-1_13
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DOI: https://doi.org/10.1007/978-3-642-19170-1_13
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