Knowledge visualizations: a tool to achieve optimized operational decision making and data integration

The overabundance of data created by modern information systems (IS) has led to a breakdown in cognitive decision-making. Without authoritative source data, commanders’ decision-making processes are hindered as they attempt to paint an accurate shared operational picture (SOP). Further impeding the decision-making process is the lack of proper interface interaction to provide a visualization that aids in the extraction of the most relevant and accurate data. Utilizing the DSS to present visualizations based on OLAP cube integrated data allow decision-makers to rapidly glean information and build their situation awareness (SA). This yields a competitive advantage to the organization while in garrison or in combat. Additionally, OLAP cube data integration enables analysis to be performed on an organization’s data-flows. This analysis is used to identify the critical path of data throughout the organization. Linking a decision-maker to the authoritative data along this critical path eliminates the many decision layers in a hierarchal command structure that can introduce latency or error into the decision-making process. Furthermore, the organization has an integrated SOP from which to rapidly build SA, and make effective and efficient decisions.http://archive.org/details/knowledgevisuali1094545877Outstanding ThesisOutstanding ThesisMajor, United States Marine CorpsCaptain, United States Marine CorpsApproved for public release; distribution is unlimited

Molecular genetic analysis of inherited kidney disease in Saudi Arabia

PhD ThesisInherited abnormalities of the kidney are frequently observed and represent a significant cause of morbidity and mortality. The globally increasing number of patients with end- stage renal disease (ESRD) urges the identification of molecular pathways involved in renal pathophysiology, to serve as targets for therapeutic intervention. Data from 2010 estimates the Saudi Arabian population to be 27 million, with one of the highest growth rates in the world. The population is characterized with high consanguinity rate, large family size, and a tribal structure. The consanguinity rate results in a high incidence of autosomal recessive genetic disorders. The population is at high risk of renal failure, with 133 incident cases per million populations per year that require renal replacement therapy. In such a population, characterization of new kidney disease gene loci using homozygosity mapping and positional cloning within consanguineous families is a powerful strategy. This study aimed to adopt this approach in order to search for known and novel molecular causes of inherited kidney diseases in the Saudi population. We studied patients and families with nephrotic syndrome, renal ciliopathies, nephrocalcinosis and renal agenesis. For nephrotic syndrome, we found that the most common genetic cause was a homozygous mutation in the NPHS2 gene. Novel and reported mutations in known nephrosis genes were detected. In a family with Bardet Biedl Syndrome, we utilized zebrafish and renal epithelial cells to determine the functional significance of a novel BBS5 mutation. In another consanguineous family with an autosomal recessive syndrome of distal renal tubular acidosis, small kidneys, and nephrocalcinosis we identified a novel locus on chromosome 2. We also describe the molecular genetic investigation of families with bilateral renal agenesis. In conclusion, in the highly consanguineous Saudi population we have utilized a variety of genetic approaches to identify and characterize novel genetic variants causing inherited renal disease.King Khalid Foundatio

COMPUTATIONAL TOOLS FOR THE DYNAMIC CATEGORIZATION AND AUGMENTED UTILIZATION OF THE GENE ONTOLOGY

Ontologies provide an organization of language, in the form of a network or graph, which is amenable to computational analysis while remaining human-readable. Although they are used in a variety of disciplines, ontologies in the biomedical field, such as Gene Ontology, are of interest for their role in organizing terminology used to describe—among other concepts—the functions, locations, and processes of genes and gene-products. Due to the consistency and level of automation that ontologies provide for such annotations, methods for finding enriched biological terminology from a set of differentially identified genes in a tissue or cell sample have been developed to aid in the elucidation of disease pathology and unknown biochemical pathways. However, despite their immense utility, biomedical ontologies have significant limitations and caveats. One major issue is that gene annotation enrichment analyses often result in many redundant, individually enriched ontological terms that are highly specific and weakly justified by statistical significance. These large sets of weakly enriched terms are difficult to interpret without manually sorting into appropriate functional or descriptive categories. Also, relationships that organize the terminology within these ontologies do not contain descriptions of semantic scoping or scaling among terms. Therefore, there exists some ambiguity, which complicates the automation of categorizing terms to improve interpretability. We emphasize that existing methods enable the danger of producing incorrect mappings to categories as a result of these ambiguities, unless simplified and incomplete versions of these ontologies are used which omit problematic relations. Such ambiguities could have a significant impact on term categorization, as we have calculated upper boundary estimates of potential false categorizations as high as 121,579 for the misinterpretation of a single scoping relation, has_part, which accounts for approximately 18% of the total possible mappings between terms in the Gene Ontology. However, the omission of problematic relationships results in a significant loss of retrievable information. In the Gene Ontology, this accounts for a 6% reduction for the omission of a single relation. However, this percentage should increase drastically when considering all relations in an ontology. To address these issues, we have developed methods which categorize individual ontology terms into broad, biologically-related concepts to improve the interpretability and statistical significance of gene-annotation enrichment studies, meanwhile addressing the lack of semantic scoping and scaling descriptions among ontological relationships so that annotation enrichment analyses can be performed across a more complete representation of the ontological graph. We show that, when compared to similar term categorization methods, our method produces categorizations that match hand-curated ones with similar or better accuracy, while not requiring the user to compile lists of individual ontology term IDs. Furthermore, our handling of problematic relations produces a more complete representation of ontological information from a scoping perspective, and we demonstrate instances where medically-relevant terms--and by extension putative gene targets--are identified in our annotation enrichment results that would be otherwise missed when using traditional methods. Additionally, we observed a marginal, yet consistent improvement of statistical power in enrichment results when our methods were used, compared to traditional enrichment analyses that utilize ontological ancestors. Finally, using scalable and reproducible data workflow pipelines, we have applied our methods to several genomic, transcriptomic, and proteomic collaborative projects

Predictive genomics in asthma management using probabilistic graphical models

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2007.Includes bibliographical references (leaves 126-142).Complex traits are conditions that, as a result of the complex interplay among genetic and environmental factors, have wide variability in progression and manifestation. Because most common diseases with high morbidity and mortality are complex traits, uncovering the genetic architecture of these traits is an important health problem. Asthma, a chronic inflammatory airway disease, is one such trait that affects over 300 million people around the world. Although there is a large amount of human genetic information currently available and expanding at a rapid pace, traditional genetic studies have not provided a concomitant understanding of complex traits, including asthma and its related phenotypes. Despite the intricate genetic background underlying complex traits, most traditional genetic studies focus on individual genetic variants. New methods that consider multiple genetic variants are needed in order to accelerate the understanding of complex traits. In this thesis, the need for better analytic approaches for the study of complex traits is addressed with the creation of a novel method. Probabilistic graphical models (PGMs) are a powerful technique that can overcome limitations of conventional association study approaches.(cont.) Going beyond single or pairwise gene interactions with a phenotype, PGMs are able to account for complex gene interactions and make predictions of a phenotype. Most PGMs have limited scalability with large genetic datasets. Here, a procedure called phenocentric Bayesian networks that is tailored for the discovery of complex multivariate models for a trait using large genomic datasets is presented. Resulting models can be used to predict outcomes of a phenotype, which allows for meaningful validation and potential applicability in a clinical setting. The utility of phenocentric Bayesian networks is demonstrated with the creation of predictive models for two complex traits related to asthma management: exacerbation and bronchodilator response. The good predictive accuracy of each model is established and shown to be superior to single gene analysis. The results of this work demonstrate the promise of using the phenocentric Bayesian networks to study the genetic architecture of complex traits, and the utility of multigenic predictive methods compared to traditional single-gene approaches.by Blanca Elena Himes.Ph.D

Toward Accessible Multilevel Modeling in Systems Biology: A Rule-based Language Concept

Promoted by advanced experimental techniques for obtaining high-quality data and the steadily accumulating knowledge about the complexity of life, modeling biological systems at multiple interrelated levels of organization attracts more and more attention recently. Current approaches for modeling multilevel systems typically lack an accessible formal modeling language or have major limitations with respect to expressiveness. The aim of this thesis is to provide a comprehensive discussion on associated problems and needs and to propose a concrete solution addressing them

Vision 2040: A Roadmap for Integrated, Multiscale Modeling and Simulation of Materials and Systems

Over the last few decades, advances in high-performance computing, new materials characterization methods, and, more recently, an emphasis on integrated computational materials engineering (ICME) and additive manufacturing have been a catalyst for multiscale modeling and simulation-based design of materials and structures in the aerospace industry. While these advances have driven significant progress in the development of aerospace components and systems, that progress has been limited by persistent technology and infrastructure challenges that must be overcome to realize the full potential of integrated materials and systems design and simulation modeling throughout the supply chain. As a result, NASA's Transformational Tools and Technology (TTT) Project sponsored a study (performed by a diverse team led by Pratt & Whitney) to define the potential 25-year future state required for integrated multiscale modeling of materials and systems (e.g., load-bearing structures) to accelerate the pace and reduce the expense of innovation in future aerospace and aeronautical systems. This report describes the findings of this 2040 Vision study (e.g., the 2040 vision state; the required interdependent core technical work areas, Key Element (KE); identified gaps and actions to close those gaps; and major recommendations) which constitutes a community consensus document as it is a result of over 450 professionals input obtain via: 1) four society workshops (AIAA, NAFEMS, and two TMS), 2) community-wide survey, and 3) the establishment of 9 expert panels (one per KE) consisting on average of 10 non-team members from academia, government and industry to review, update content, and prioritize gaps and actions. The study envisions the development of a cyber-physical-social ecosystem comprised of experimentally verified and validated computational models, tools, and techniques, along with the associated digital tapestry, that impacts the entire supply chain to enable cost-effective, rapid, and revolutionary design of fit-for-purpose materials, components, and systems. Although the vision focused on aeronautics and space applications, it is believed that other engineering communities (e.g., automotive, biomedical, etc.) can benefit as well from the proposed framework with only minor modifications. Finally, it is TTT's hope and desire that this vision provides the strategic guidance to both public and private research and development decision makers to make the proposed 2040 vision state a reality and thereby provide a significant advancement in the United States global competitiveness

RECENT ADVANCES IN MOLECULAR MEDICINE AND TRANSLATIONAL RESEARCH

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