Indiana University-Purdue University Indianapolis (IUPUI) / Bayesian networks, one of the most widely used techniques to understand or predict the future by making use of current or previous data, have gained credence over the last decade for their ability to simulate large gene expression datasets to track and predict the reasons for changes in biological systems. In this work, we present a dynamic Bayesian model with gene annotation scores such as the gene characterization index (GCI) and the GenCards inferred functionality score (GIFtS) to understand and assess the prediction performance of the model by incorporating prior knowledge. Time-course breast cancer data including expression data about the genes in the breast cell-lines when treated with doxorubicin is considered for this study. Bayes server software was used for the simulations in a dynamic Bayesian environment with 8 and 19 genes on 12 different data combinations for each category of gene set to predict and understand the future time- course expression profiles when annotation scores are incorporated into the model. The 8-gene set predicted the next time course with r>0.95, and the 19-gene set yielded a value of r>0.8 in 92% cases of the simulation experiments. These results showed that incorporating prior knowledge into the dynamic Bayesian model for simulating the time- course expression data can improve the prediction performance when sufficient apriori parameters are provided.

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Document classification is used to sort and label documents. This gives users quicker access to relevant data. Users that work with large inflow of documents spend time filing and categorizing them to allow for easier procurement. The Automatic Classification and Document Filing (ACDF) system proposed here is designed to allow users working with files or documents to rely on the system to classify and store them with little manual attention. By using a system built on Hidden Markov Models, the documents in a smaller desktop environment are categorized with better results than the traditional Naive Bayes implementation of classification.

This thesis presents an automatic soil texture classification system using hyperspectral soil signals and wavelet-based statistical models. Previous soil texture classification systems are closely related to texture classification methods, which use images for training and testing. Although using image-based algorithms is a straightforward way to conduct soil texture classification, our research shows that it does not provide reliable and consistent results. Rather, we develop a novel system using hyperspectral soil textures, better known as hyperspectral soil signals, which provide rich information and intrinsic properties about soil textures. Hyperspectral soil textures, in their very nature, are nonstationary and time-varying. Therefore, the wavelet transform, which is proven to be successful in such applications, is incorporated. In this study, we incorporate two wavelet-domain statistical models, namely, the maximum likelihood (ML) and the hidden Markov model (HMM) for the classification task. Experimental results show that this method is reliable and robust. It is also more effective and efficient in terms of practical implementation than the traditional image-based methods.

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<p>This thesis presents the design and implementation of a source code generator for dealing with Bayesian statistics. The specific focus of this case study is to produce usable source code for handling Hidden Markov Models (HMMs) from a Domain Specific Language (DSL).</p> <p>Domain specific languages are used to allow domain experts to design their source code from the perspective of the problem domain. The goal of designing in such a way is to increase the development productivity without requiring extensive programming knowledge.</p> / Master of Applied Science (MASc)

NIBP is a prototype member of a novel protein family. It forms a novel subcomplex of NIK-NIBP-IKKB and enhances cytokine-induced IKKB-mediated NFKB activation. It is also named TRAPPC9 as a key member of trafficking particle protein (TRAPP) complex II, which is essential in trans-Golgi networking (TGN). The signaling pathways and molecular mechanisms for NIBP actions remain largely unknown. The aim of this research is to identify potential proteins interacting with NIBP, resulting in the regulation of NFKB signaling pathways and other unknown signaling pathways. At the laboratory of Dr. Wenhui Hu in the Department of Neuroscience, Temple University, sixteen partner proteins were experimentally identified that potentially bind to NIBP. NIBP is a novel protein with no entry in the Protein Data Bank. From a computational and bioinformatics standpoint, we use prediction of secondary structure and protein disorder as well as homology-based structural modeling approaches to create a hypothesis on protein-protein interaction between NIBP and the partner proteins. Structurally, NIBP contains three distinct regions. The first region, consisting of 200 amino acids, forms a hybrid helix and beta sheet-based domain possibly similar to Sybindin domain. The second region comprised of approximately 310 residues, forms a tetratrico peptide repeat (TPR) zone. The third region is a 675 residue long all beta sheet and loops zone with as many as 35 strands and only 2 helices, shared by Gryzun-domain containing proteins. It is likely to form two or three beta sheet sandwiches. The TPR regions of many proteins tend to bind to the peptides from disordered regions of other proteins. Many of the 16 potential binding proteins have high levels of disorder. These data suggest that the TPR region in NIBP most likely binds with many of these 16 proteins through peptides and other domains. It is also possible that the Sybindin-like domain and the Gryzun-like domain containing beta sheet sandwiches bind to some of these proteins. / Bioengineering