373 research outputs found

    Managing Requirement Volatility in an Ontology-Driven Clinical LIMS Using Category Theory. International Journal of Telemedicine and Applications

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    Requirement volatility is an issue in software engineering in general, and in Web-based clinical applications in particular, which often originates from an incomplete knowledge of the domain of interest. With advances in the health science, many features and functionalities need to be added to, or removed from, existing software applications in the biomedical domain. At the same time, the increasing complexity of biomedical systems makes them more difficult to understand, and consequently it is more difficult to define their requirements, which contributes considerably to their volatility. In this paper, we present a novel agent-based approach for analyzing and managing volatile and dynamic requirements in an ontology-driven laboratory information management system (LIMS) designed for Web-based case reporting in medical mycology. The proposed framework is empowered with ontologies and formalized using category theory to provide a deep and common understanding of the functional and nonfunctional requirement hierarchies and their interrelations, and to trace the effects of a change on the conceptual framework.Comment: 36 Pages, 16 Figure

    A framework for analyzing changes in health care lexicons and nomenclatures

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    Ontologies play a crucial role in current web-based biomedical applications for capturing contextual knowledge in the domain of life sciences. Many of the so-called bio-ontologies and controlled vocabularies are known to be seriously defective from both terminological and ontological perspectives, and do not sufficiently comply with the standards to be considered formai ontologies. Therefore, they are continuously evolving in order to fix the problems and provide valid knowledge. Moreover, many problems in ontology evolution often originate from incomplete knowledge about the given domain. As our knowledge improves, the related definitions in the ontologies will be altered. This problem is inadequately addressed by available tools and algorithms, mostly due to the lack of suitable knowledge representation formalisms to deal with temporal abstract notations, and the overreliance on human factors. Also most of the current approaches have been focused on changes within the internal structure of ontologies, and interactions with other existing ontologies have been widely neglected. In this research, alter revealing and classifying some of the common alterations in a number of popular biomedical ontologies, we present a novel agent-based framework, RLR (Represent, Legitimate, and Reproduce), to semi-automatically manage the evolution of bio-ontologies, with emphasis on the FungalWeb Ontology, with minimal human intervention. RLR assists and guides ontology engineers through the change management process in general, and aids in tracking and representing the changes, particularly through the use of category theory. Category theory has been used as a mathematical vehicle for modeling changes in ontologies and representing agents' interactions, independent of any specific choice of ontology language or particular implementation. We have also employed rule-based hierarchical graph transformation techniques to propose a more specific semantics for analyzing ontological changes and transformations between different versions of an ontology, as well as tracking the effects of a change in different levels of abstractions. Thus, the RLR framework enables one to manage changes in ontologies, not as standalone artifacts in isolation, but in contact with other ontologies in an openly distributed semantic web environment. The emphasis upon the generality and abstractness makes RLR more feasible in the multi-disciplinary domain of biomedical Ontology change management

    Relations in biomedical ontologies

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    To enhance the treatment of relations in biomedical ontologies we advance a methodology for providing consistent and unambiguous formal definitions of the relational expressions used in such ontologies in a way designed to assist developers and users in avoiding errors in coding and annotation. The resulting Relation Ontology can promote interoperability of ontologies and support new types of automated reasoning about the spatial and temporal dimensions of biological and medical phenomena

    Thirty years of artificial intelligence in medicine (AIME) conferences: A review of research themes

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    Over the past 30 years, the international conference on Artificial Intelligence in MEdicine (AIME) has been organized at different venues across Europe every 2 years, establishing a forum for scientific exchange and creating an active research community. The Artificial Intelligence in Medicine journal has published theme issues with extended versions of selected AIME papers since 1998

    Bioinformatics applied to human genomics and proteomics: development of algorithms and methods for the discovery of molecular signatures derived from omic data and for the construction of co-expression and interaction networks

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    [EN] The present PhD dissertation develops and applies Bioinformatic methods and tools to address key current problems in the analysis of human omic data. This PhD has been organised by main objectives into four different chapters focused on: (i) development of an algorithm for the analysis of changes and heterogeneity in large-scale omic data; (ii) development of a method for non-parametric feature selection; (iii) integration and analysis of human protein-protein interaction networks and (iv) integration and analysis of human co-expression networks derived from tissue expression data and evolutionary profiles of proteins. In the first chapter, we developed and tested a new robust algorithm in R, called DECO, for the discovery of subgroups of features and samples within large-scale omic datasets, exploring all feature differences possible heterogeneity, through the integration of both data dispersion and predictor-response information in a new statistic parameter called h (heterogeneity score). In the second chapter, we present a simple non-parametric statistic to measure the cohesiveness of categorical variables along any quantitative variable, applicable to feature selection in all types of big data sets. In the third chapter, we describe an analysis of the human interactome integrating two global datasets from high-quality proteomics technologies: HuRI (a human protein-protein interaction network generated by a systematic experimental screening based on Yeast-Two-Hybrid technology) and Cell-Atlas (a comprehensive map of subcellular localization of human proteins generated by antibody imaging). This analysis aims to create a framework for the subcellular localization characterization supported by the human protein-protein interactome. In the fourth chapter, we developed a full integration of three high-quality proteome-wide resources (Human Protein Atlas, OMA and TimeTree) to generate a robust human co-expression network across tissues assigning each human protein along the evolutionary timeline. In this way, we investigate how old in evolution and how correlated are the different human proteins, and we place all them in a common interaction network. As main general comment, all the work presented in this PhD uses and develops a wide variety of bioinformatic and statistical tools for the analysis, integration and enlighten of molecular signatures and biological networks using human omic data. Most of this data corresponds to sample cohorts generated in recent biomedical studies on specific human diseases

    Enriching and designing metaschemas for the UMLS semantic network

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    The disparate terminologies used by various biomedical applications or professionals make the communication between them more difficult. The Unified Medical Language System (UMLS) of the National Library of Medicine (NLM) is an attempt to integrate different medical terminologies into a unified representation framework to improve decision making and the quality of patient care as well as research in the health-care field. Metathesaurus (META) and Semantic Network (SN) are two main components of the UMLS system, where the SN provides a high-level abstract of the concepts in the META. This dissertation addresses three problems of the SN. First, the SN\u27s two-tree structure is restrictive because it does not allow a semantic type to be a specialization of several other semantic types. This restriction leads to the omission of some subsumption knowledge in the SN. Secondly, the SN is large and complex for comprehension purposes and it does not come with a pictorial representation for users. As a partial solution for this problem, several metaschemas were previously built as higher-level abstractions for the SN to help users\u27 orientation. Third, there is no efficient method to evaluate each metaschema. There is no technique to obtain a consolidated metaschema acceptable for a majority of the UMLS\u27s users. In this dissertation work the author attacked the described problems by using the following approaches. (1) The SN was expanded into the Enriched Semantic Network (ESN), a multiple subsumption structure with a directed acyclic graph (DAG) IS-A hierarchy, allowing a semantic type to have multiple parents. New viable IS-A links were added as warranted. Two methodologies were presented to identify and add new viable IS-A links. The ESN serves as an extended high-level abstract of the META. (2) The ESN\u27s semantic relationship distribution and concept configuration were studied. Rules were defined to derive the ESN\u27s semantic relationship distribution from the current SN\u27s semantic relationship distribution. A mapping function was defined to map the SN\u27s concept configuration to the ESN\u27s concept configuration, avoiding redundant classifications in the ESN\u27s concept configuration. (3) Several new metaschemas for the SN and the ESN were built and evaluated based on several different partitioning techniques. Each of these metaschema can serve as a higher-level abstraction of the SN (or the ESN)

    Understanding Addiction

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    The addiction literature is fraught with conceptual confusions, stalled debates, and an unfortunate lack of clear and careful attempts to delineate the phenomenon of addiction in a way that might lead to consensus. My dissertation has two overarching aims, one metaphysical and one practical. The first aim is to defend an account of addiction as the systematic disposition to fail to control one’s desires to engage in certain types of behaviors. I defend the inclusion of desires and impaired control in the definition, and I flesh out the notion of systematicity central to my dispositionalist framework. I engage the so-called ‘disease vs. choice’ debate, criticizing its presupposition that we are dealing here with a dichotomy and arguing that the movement towards a middle ground is the right track to take. I explain how the dispositionalist account can capture this middle ground and how it serves to expand upon existing views, in particular by filling in the metaphysical details. The second aim is to show how the account I defend can help to unify the extant views and disciplinary perspectives in the literature. Both the dispositionalist aspect of my framework and the methodology adopted (applied ontology and systematic metaphysics) can move the literature towards both substantive and methodological unification. This will help to clear up conceptual confusions, resolve (or sometimes dissolve) apparently intractable disputes, situate different research perspectives with respect to each other, facilitate interdisciplinary dialogue, and help to frame important questions about addiction. Finally, I offer the beginnings of an ontology of addiction, which will provide a terminologically well-structured guide to the addiction literature in a way that will facilitate more effective and efficient communication and data management across disciplines

    Responses of Bats to White-Nose Syndrome and Implications for Conservation

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    The appearance and spread of emerging infectious diseases pose a significant threat to wildlife populations worldwide having resulted in declines far surpassing those in recorded history. White-nose syndrome (WNS), caused by Pseudogymnoascus destructans (Pd), is currently one of the most pervasive wildlife diseases, with devastating impacts on several North American bat species. Since its initial detection in 2006, Pd has spread rapidly across North America and population declines at hibernating sites have been severe; however, mortality rates from WNS vary among species. While environmental conditions in hibernacula may be strong predictors of disease impacts on individual species, variation in susceptibility that cannot be explained by environmental conditions alone suggests that other processes potentially play a role in species susceptibility to the disease. This work attempts to help disentangle the influence of the other processes impacting species susceptibility, as well as provide a framework for the conservation of a potentially threatened species. The first chapter specifically addresses the role of the bat skin microbiome in response to Pd presence with results suggesting that microbiome-host interactions may determine the likelihood of infection for Myotis lucifugus, a heavily impacted species. The second chapter assesses the population genetics of a threatened bat species, Myotis septentrionalis, with results uncovering genetic admixture throughout the species range as well as genes putatively under selection in response to WNS. The third chapter provides a framework for the conservation and management of Perimyotis subflavus using what is currently understood about WNS and its impact on this potentially threatened species within the field of molecular biology. Collectively, this work contributes to a field of research that exists to better understand and potentially help mitigate this devastating wildlife disease

    Sense-able Hauntings: Ethics and Narratives in Ornithological Specimen Preservation at Yale\u27s Peabody Museum

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    Until I was standing in between the rows of towering metal specimen cabinets, I didn’t understand how many birds the Yale Peabody Museum holds. Like most people, I had only experienced the display side of museums: its dioramas and glass cases. These displays, while made to embody a certain set of interests, priorities, and values, still serve an obscuring function—they vastly underrepresent the museum’s total collections. In their ornithology collection alone, the Peabody currently holds more than 152,000 bird skins, bones, eggs, nests, and other avian fragments. The Peabody staff members who maintain the ornithology specimen collections are distinct from those who create the displays; as a result, the knowledge produced through museum-based research is shaped by the interests and imaginations of who has collection access. This thesis seeps into, around, and under the locked spaces of museums’ death-rich collections. I consider how institutional actors experience complex emotions about life and death, nature and culture, as they labor to maintain what is—on one level—an avian crypt. Understanding how specimen collections embody specific ethical and ontological orientations to the world offers an opportunity to reimagine the science done within (and beyond) sites like the Peabody
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