ATD:Bayesian data mining approaches for Biological threat detection

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Recent developments of DNA-based methods are reliable tools for detecting and characterizing biological agents. Pathogen detection in this way is challenging, because there are few genetic differences that distinguish a pathogen from a closely related nonpathogenic organism. The principal investigators propose Bayesian detection methods combining prior biological information as well as data from different biological platforms. Gene expression microarray data as well as massively parallel signature sequencing (MPSS) will be combined by a novel data fusion method to perform proper inference about the unknowns. Gene networks models will be developed to identify the dependence and interactions among the genes. The principal investigators will develop hierarchical Bayesian models where the data from different sources will be related to each other by conditional models at different stages of the hierarchy. They will consider nonparametric models which wil create an automatic clustering of the genes. Novel Bayesian graph clustering model will be developed by combining local Gaussian models and the Dirichlet process prior. Due to complexity of the problems, the joint posterior distribution of the unknown parameters will not be explicitly available hence Markov Chain Monte Carlo (MCMC) based computation methods will be used to draw samples from the posterior distribution."Terrorists are likely to use a weapon of mass destruction somewhere in the world in the next five years and they are more likely to use a biological weapon than a nuclear one -- and the results could be devastating," the chairman of the a blue-ribbon panel assembled by Congress told to media on 2nd December, 2008. Biological attack is more likely than a nuclear one because it would be easier to carry out. Historically disease-causing microbes have taken their toll on human populations, sometimes in devastating numbers. These disease causing pathogens can be utilized as biological weapons. One of the scientific movements to reduce the biological thereat will be the development of methods for proper detection and characterization of those biological agents that can be used as weapons. The intellectual merit of the proposed activity is that it will provide general and consistent frameworks for deadly pathogen detection using genomic data. The efficient tools and models which will be developed through this project will be utilized to reduce the biological threat generated from these deadly pathogens. The methods proposed here is not only applicable to the scenarios described in this proposal, but also to a wide variety of basic science and biomedical problems with genomic data. Understanding regulatory networks and gene interactions will have significant impact on the development of molecular therapeutic approaches targeted against cellular abnormalities.