Biological systems constantly evolve and adapt in response to changed environment and external stimuli at the molecular and genomic levels. Building statistical models that characterize such dynamic changes in biological systems is one of the key objectives in bioinformatics and computational biology. Recent advances in high-throughput genomic and molecular profiling technologies such as gene expression and and copy number microarrays provide ample opportunities to study cellular activities at the individual gene and network levels. The aim of this dissertation is to formulate mathematically dynamic changes in biological networks and DNA copy numbers, to develop machine learning algorithms to learn these statistical models from high-throughput biological data, and to demonstrate their applications in systems biological studies. The first part (Chapters 2-4) of the dissertation focuses on the dynamic changes taking placing at the biological network level. Biological networks are context-specific and dynamic in nature. Under different conditions, different regulatory components and mechanisms are activated and the topology of the underlying gene regulatory network changes. We report a differential dependency network (DDN) analysis to detect statistically significant topological changes in the transcriptional networks between two biological conditions. Further, we formalize and extend the DDN approach to an effective learning strategy to extract structural changes in graphical models using l1-regularization based convex optimization. We discuss the key properties of this formulation and introduce an efficient implementation by the block coordinate descent algorithm. Another type of dynamic changes in biological networks is the observation that a group of genes involved in certain biological functions or processes coordinate to response to outside stimuli, producing distinct time course patterns. We apply the echo stat network, a new architecture of recurrent neural networks, to model temporal gene expression patterns and analyze the theoretical properties of echo state networks with random matrix theory. The second part (Chapter 5) of the dissertation focuses on the changes at the DNA copy number level, especially in cancer cells. Somatic DNA copy number alterations (CNAs) are key genetic events in the development and progression of human cancers, and frequently contribute to tumorigenesis. We propose a statistically-principled in silico approach, Bayesian Analysis of COpy number Mixtures (BACOM), to accurately detect genomic deletion type, estimate normal tissue contamination, and accordingly recover the true copy number profile in cancer cells. / Ph. D.

This dissertation investigates algorithmic techniques for temporal process discovery in many domains. Many different formalisms have been proposed for modeling temporal processes such as motifs, dynamic Bayesian networks and partial orders, but the direct inference of such models from data has been computationally intensive or even intractable. In this work, we propose the mining of frequent episodes as a bridge to inferring more formal models of temporal processes. This enables us to combine the advantages of frequent episode mining, which conducts level wise search over constrained spaces, with the formal basis of process representations, such as probabilistic graphical models and partial orders. We also investigate the mining of frequent episodes in infinite data streams which further expands their applicability into many modern data mining contexts. To demonstrate the usefulness of our methods, we apply them in different problem contexts such as: sensor networks in data centers, multi-neuronal spike train analysis in neuroscience, and electronic medical records in medical informatics. / Ph. D.

The goal of this study was to examine the use of a technological and engineering design based learning (T/E DBL) challenge as a strategy for facilitating student comprehension of nonfiction/informational text inclusive of graphical devices. The data for this mixed methods exploratory case study were collected using a variety of instruments which assessed the prior knowledge, general graphical device comprehension, and reading comprehension of both familiar and unfamiliar texts in order to form a detailed picture of the six participants throughout the study. The six participants were examined as whole group and as reading level dyads (below, on, and above grade level) as they progressed through three T/E DBL challenges that were developed to support graphical device comprehension instruction. T/E DBL was found to increase reader text interactions and graphical device usage, support the development of general graphical device comprehension for diagrams and tables, improve comprehension of unfamiliar science texts, and provide particular benefit to below grade level readers. The results of this study demonstrate the need for further research into the benefits of T/E DBL for reading instruction, particularly of graphical devices. This research should include a further exploration of the potential benefits for graphical device comprehension and comprehension of unfamiliar science and engineering texts that include graphical devices, as well as the curricular, training, and implementation needs. / Doctor of Philosophy / This study examined how challenging fifth grade students to design a technology to meet an engineering need can support student understanding of nonfiction/informational texts which include informational graphics (graphical devices). The participants of this study were asked to create designs of different types of technology which would benefit from the information in the provided informational texts and graphics. A variety of data were gathered on six fifth grade participants as they worked through a serious of design challenges that were paired with reading passages that included graphics (graphical devices). Graphical device instruction using design challenges was found to increase readers' interactions with texts and their usage of graphical devices, support the development of comprehension for diagrams and tables, improve comprehension of unfamiliar science texts, and provide particular benefit to below grade level readers. The results of this study demonstrate the need for further research into the benefits of using design challenges for reading instruction, particularly of graphical devices.

Interested citizens use the Internet for, among other purposes, expressing their opinions and views about political issues and local concerns. There is much expression by citizens in web logs (or blogs). Blogs are a form of individual expression, publicly available and constantly updated. Blog entries may contain a variety of topics of discussion. Two topics are the focus of this thesis: political and local issues. Often blogs are aggregated into regional collections. These aggregated sites are a good source for local and regional discussions. However, because the discussions are only implicitly connected, tools are needed to identify similarity in otherwise individual blog entries. Blog visualizations can help address this problem. We have created a tool, VizBlog that supports the task of local blog discussion discovery. This blog visualization tool visually presents information in a way that helps users identify blog entry clusters of similar content, helps citizens find other citizens opinions, and also helps government officials identify local hot issues. This research seeks to: a) validate the accuracy of the automated similarity classification done by VizBlog; b) evaluate the usability of VizBlog; and c) study the characteristics of local conversations scattered in a series of regional blogs. The results of the evaluation showed that VizBlog did make it easy for users to identify topics of interest from the visualization, in addition to providing insight on ongoing discussion taking place in regional blogs. In addition, the automated similarity computation was validated when compared to classification done by humans. Finally, the thesis discusses the findings of the structure of the regional blogosphere. / Master of Science

In dieser Arbeit habe ich eine Software entwickelt, mit der ein realer Funktionsgenerator ferngesteuert werden kann. Als Grundlage diente die Programmiersprache Python und das Model-View-Controller-Modell. Ziel war es, eine grafische Benutzeroberfläche zu erstellen, die dem Gerät möglichst ähnlich ist, und einen Steuerungsmechanismus für die Befehlsübermittlung zu implementieren, um den digitalen Unterricht zu unterstützen.:Vorwort V Abbildungsverzeichnis IX Tabellenverzeichnis XI Formelverzeichnis XII Abkürzungsverzeichnis XIII 1 Einleitung 1 2 Theoretische Grundlagen 6 2.1 Fernsteuerung von Laborgeräten 6 2.2 Funktionsgenerator 7 2.3 Programmieren mit Python 8 2.4 Grundlagen des Model-View-Controller-Modells 9 2.4.1 Betrachtung der einzelnen Komponenten 9 2.4.2 Wechselwirkungen zwischen den Komponenten 10 3 Beschreibung des Untersuchungsgegenstandes 15 3.1 Analyse des Ist-Standes 15 3.1.1 Laborpraktika 15 3.1.2 Technische Daten des Funktionsgenerators 17 3.1.3 Stand der Digitalisierung 17 3.2 Soll-Zustand 19 3.3 Analyse des Funktionsgenerators 19 3.3.1 Aufbau des Funktionsgenerators 20 3.3.2 Funktionsanalyse des Funktionsgenerators 23 4 Rechentechnische Implementierung 34 4.1 Vorbereitung der Softwareentwicklung 34 4.1.1 Auswahl spezifischer Programmierwerkzeuge 34 4.1.2 Auswahl einer Entwicklungsumgebung 44 4.1.3 Aufstellung von Programmierungsprämissen 47 4.2 Softwaretechnische Umsetzung des MVC-Modells 51 4.2.1 View - grafischen Benutzeroberfläche 51 4.2.2 Model - Datenmodell 67 4.2.3 Controller - Steuerungslogik 82 4.2.4 Implementierung von Backend-Funktionen 98 5 Prototypische Inbetriebnahme 103 6 Zusammenfassung 105 7 Ausblick 107 Literaturverzeichnis XIV Anhang XVII

The purpose of this research thesis was to develop a framework and methodology for the design of a graphical user interface to be used by air traffic controllers. The interface is intended to be only a part of a complete Advisory System designed to supplement the tasks of terminal area air traffic controllers. This research addresses many of the human factors issues associated with the development of the display. The research takes a user-perspective and applies the principles of rapid prototyping to develop the framework for the design of the interface. Attention is also given to the previous research that explores the implications of automating various air traffic control tasks. Finally, a prototype system was developed to fulfill one of the primary rapid prototyping steps. The prototype displays the general format for the various advisories and presents three typical scenarios where the system may be of particular use. In the future, the prototype can be used to gather additional information on the opinions and requirements of the future system users - air traffic controllers. It is anticipated that moderate benefits can be attained through the implementation of such a system, provided that the interface satisfies the user requirements. / Master of Science

A Factor Graph (FG -- http://en.wikipedia.org/wiki/Factor_graph) is a structure used to find solutions to problems that can be represented as a Probabilistic Graphical Model (PGM). They consist of interconnected variable nodes and factor nodes, which iteratively compute and pass messages to each other. FGs can be applied to solve decoding of forward error correcting codes, Markov chains and Markov Random Fields, Kalman Filtering, Fourier Transforms, and even some games such as Sudoku. In this paper, a framework is presented for rapid prototyping of hardware implementations of FG-based applications. The FG developer specifies aspects of the application, such as graphical structure, factor computation, and message passing algorithm, and the framework returns a design. A system of Python scripts and Verilog Hardware Description Language templates together are used to generate the HDL source code for the application. The generated designs are vendor/platform agnostic, but currently target the Xilinx Virtex-6-based ML605. The framework has so far been primarily applied to construct Low Density Parity Check (LDPC) decoders. The characteristics of a large basket of generated LDPC decoders, including contemporary 802.11n decoders, have been examined as a verification of the system and as a demonstration of its capabilities. As a further demonstration, the framework has been applied to construct a Sudoku solver. / Master of Science

Gene network modeling is one of the major goals of systems biology research. Gene network modeling targets the middle layer of active biological systems that orchestrate the activities of genes and proteins. Gene network modeling can provide critical information to bridge the gap between causes and effects which is essential to explain the mechanisms underlying disease. Among the network construction tasks, the rewiring of relevant network structure plays critical roles in determining the behavior of diseases. To systematically characterize the selectively activated regulatory components and mechanisms, the modeling tools must be able to effectively distinguish significant rewiring from random background fluctuations. While differential dependency networks cannot be constructed by existing knowledge alone, effective incorporation of prior knowledge into data-driven approaches can improve the robustness and biological relevance of network inference. Existing studies on protein-protein interactions and biological pathways provide constantly accumulated rich domain knowledge. Though novel incorporation of biological prior knowledge into network learning algorithms can effectively leverage domain knowledge, biological prior knowledge is neither condition-specific nor error-free, only serving as an aggregated source of partially-validated evidence under diverse experimental conditions. Hence, direct incorporation of imperfect and non-specific prior knowledge in specific problems is prone to errors and theoretically problematic. To address this challenge, we propose a novel mathematical formulation that enables incorporation of prior knowledge into structural learning of biological networks as Gaussian graphical models, utilizing the strengths of both measurement data and prior knowledge. We propose a novel strategy to estimate and control the impact of unavoidable false positives in the prior knowledge that fully exploits the evidence from data while obtains "second opinion" by efficient consultations with prior knowledge. By proposing a significance assessment scheme to detect statistically significant rewiring of the learned differential dependency network, our method can assign edge-specific p-values and specify edge types to indicate one of six biological scenarios. The data-knowledge jointly inferred gene networks are relatively simple to interpret, yet still convey considerable biological information. Experiments on extensive simulation data and comparison with peer methods demonstrate the effectiveness of knowledge-fused differential dependency network in revealing the statistically significant rewiring in biological networks, leveraging data-driven evidence and existing biological knowledge, while remaining robust to the false positive edges in the prior knowledge. We also made significant efforts in disseminating the developed method tools to the research community. We developed an accompanying R package and Cytoscape plugin to provide both batch processing ability and user-friendly graphic interfaces. With the comprehensive software tools, we apply our method to several practically important biological problems to study how yeast response to stress, to find the origin of ovarian cancer, and to evaluate the drug treatment effectiveness and other broader biological questions. In the yeast stress response study our findings corroborated existing literatures. A network distance measurement is defined based on KDDN and provided novel hypothesis on the origin of high-grade serous ovarian cancer. KDDN is also used in a novel integrated study of network biology and imaging in evaluating drug treatment of brain tumor. Applications to many other problems also received promising biological results. / Ph. D.

In this dissertation, we develop innovative methods for analyzing high-dimensional, heterogeneous functional data, focusing specifically on uncovering hidden patterns and network structures within such complex data. We utilize functional graphical models (FGMs) to explore the conditional dependence structure among random elements. We mainly focus on the following three research projects. The first project combines the strengths of FGMs with finite mixture of regression models (FMR) to overcome the challenges of estimating conditional dependence structures from heterogeneous functional data. This novel approach facilitates the discovery of latent patterns, proving particularly advantageous for analyzing complex datasets, such as brain imaging studies of autism spectrum disorder (ASD). Through numerical analysis of both simulated data and real-world ASD brain imaging, we demonstrate the effectiveness of our methodology in uncovering complex dependencies that traditional methods may miss due to their homogeneous data assumptions. Secondly, we address the challenge of variable selection within FMR in high-dimensional settings by proposing a joint variable selection technique. This technique employs a penalized expectation-maximization (EM) algorithm that leverages shared structures across regression components, thereby enhancing the efficiency of identifying relevant predictors and improving the predictive ability. We further expand this concept to mixtures of functional regressions, employing a group lasso penalty for variable selection in heterogeneous functional data. Lastly, we recognize the limitations of existing methods in testing the equality of multiple functional graphs and develop a novel, permutation-based testing procedure. This method provides a robust, distribution-free approach to comparing network structures across different functional variables, as illustrated through simulation studies and functional magnetic resonance imaging (fMRI) analysis for ASD. Hence, these research works provide a comprehensive framework for functional data analysis, significantly advancing the estimation of network structures, functional variable selection, and testing of functional graph equality. This methodology holds great promise for enhancing our understanding of heterogeneous functional data and its practical applications. / Doctor of Philosophy / This study introduces innovative techniques for analyzing complex, high-dimensional functional data, such as functional magnetic resonance imaging (fMRI) data from the brain. Our goal is to reveal underlying patterns and network connections, particularly in the context of autism spectrum disorder (ASD). In functional data, we treat each signal curve from various locations as a single data point. These datasets are characterized by high dimensionality, with the number of model parameters exceeding the sample size. We employ functional graphical models (FGMs) to investigate the conditional dependencies among data elements. Our approach combines FGMs with finite mixture of regression models (FMR), allowing us to uncover hidden patterns that traditional methods assuming homogeneity might miss. Additionally, we introduce a new method for selecting relevant variables in high-dimensional regression contexts. This method enhances prediction accuracy by utilizing shared information among regression components. Furthermore, we develop a robust testing framework to facilitate the comparison of network structures between groups without relying on distribution assumptions. This enables precise evaluations of functional graphs. Hence, our research works contribute to a deeper understanding of complex, diverse functional data, paving the way for novel insights across various fields.

No / The results from previous experiments using structured musical stimuli to communicate coordinate locations within a graphical grid, navigation of an auditory cursor and simple shapes are used as a basis for further exploratory research to communicate diagrams. An experimental framework program (called AudioGraph) provided a platform for investigating musical information processing for blind users. Under this platform, simple arrangements of shapes (forming diagrams) were communicated to users using structured musical stimuli. Meaningfully arranged graphical shapes (at least for the visual sense) were communicated in the absence, and in the presence of a particular perceptual context or different perceptual contexts. The results indicated that perceptual context played an important role in the interpretation of the structured musical stimuli that communicated simple diagrams. The paper concludes with a discussion on the implications of the results, the role of context and the use of structured musical stimuli to communicate graphical information to visually impaired users.