Master of Science

thesisWhen coupled with a common information model, a common terminology for clinical decision support (CDS) and electronic clinical quality measurement (eCQM) could greatly facilitate the distributed development and sharing of CDS and eCQM knowledge resources. To enable such scalable knowledge authoring and sharing, we systematically developed an extensible and standards-based terminology for CDS and eCQM in the context of the HL7 Virtual Medical Record (vMR) information model. The development of this terminology entailed three steps: (1) systematic, physician-curated concept identification from sources such as the Health Information Technology Standards Panel (HITSP) and the SNOMED-CT CORE problem list; (2) concept de-duplication leveraging the Unified Medical Language System (UMLS) MetaMap and Metathesaurus; and (3) systematic concept naming using standard terminologies and heuristic algorithms. This process generated 3,046 concepts spanning 68 domains. Evaluation against representative CDS and eCQM resources revealed approximately 50-70% concept coverage, indicating the need for continued expansion of the terminology

Representing Ontogeny Through Ontology: A Developmental Biologist’s Guide to The Gene Ontology

Developmental biology, like many other areas of biology, has undergone a dramatic shift in the perspective from which developmental processes are viewed. Instead of focusing on the actions of a handful of genes or functional RNAs, we now consider the interactions of large functional gene networks and study how these complex systems orchestrate the unfolding of an organism, from gametes to adult. Developmental biologists are beginning to realize that understanding ontogeny on this scale requires the utilization of computational methods to capture, store and represent the knowledge we have about the underlying processes. Here we review the use of the Gene Ontology (GO) to study developmental biology. We describe the organization and structure of the GO and illustrate some of the ways we use it to capture the current understanding of many common developmental processes. We also discuss ways in which gene product annotations using the GO have been used to ask and answer developmental questions in a variety of model developmental systems. We provide suggestions as to how the GO might be used in more powerful ways to address questions about development. Our goal is to provide developmental biologists with enough background about the GO that they can begin to think about how they might use the ontology efficiently and in the most powerful ways possible

Méthodes sémantiques pour la comparaison inter-espèces de voies métaboliques (application au métabolisme des lipides chez l'humain, la souris et la poule)

La comparaison inter-espèces de voies métaboliques est une problématique importante en biologie. Actuellement, les connaissances sont générées à partir d'expériences sur un nombre relativement limité d'espèces dites modèles. Mieux connaître une espèce permet de valider ou non une inférence faite à partir de ces données expérimentales et de déterminer si ou dans quelle mesure des résultats obtenus sur une espèce modèle peuvent être transposés à une autre espèce. Cette thèse propose une méthode de comparaison inter-espèces de voies métaboliques. Elle compare chaque étape d'une voie métabolique en exploitant les annotations dans Gene Ontology qui leur sont associées. Ce travail valide l'intérêt des mesures de similarités sémantiques pour interpréter ces annotations, propose d'utiliser conjointement une mesure de particularité sémantique et propose une méthode basée sur des motifs de similarité et de particularité pour interpréter chaque étape de voie métabolique. De nombreuses mesures sémantiques quantifient la similarité entre des produits de gènes en fonction des annotations qu'ils ont en commun. Nous en avons identifié et utilisé une adaptée à la problématique de comparaison inter-espèces. En se focalisant sur la part commune aux produits de gènes comparés, les mesures de similarité sémantiques ignorent les caractéristiques spécifiques d'un seul produit de gène. Or la comparaison inter-espèces de voies métaboliques se doit de quantifier non seulement la similarité des produits de gènes qui interviennent dans celles-ci, mais également leurs particularités. Nous avons développé une mesure de particularité sémantique répondant à cette problématique. Pour chaque étape de voie métabolique, nous calculons un profil composé de sa valeur de similarité et de ses deux valeurs de particularité sémantiques. Il n'est pas possible d'établir formellement que deux produits de gènes sont similaires ou que l'un d'eux a des particularités significatives sans disposer d'un seuil de similarité et d'un seuil de particularité. Jusqu'à présent, ces interprétations se faisaient sur la base d'un seuil implicite ou arbitraire. Pour combler ce manque, nous avons développé une méthode de définition de seuils pour les mesures de similarité et de particularité sémantiques. Nous avons enfin appliqué une mesure de similarité inter-espèces et notre mesure de particularité pour comparer le métabolisme des lipides entre l'Homme, la souris et la poule. Nous avons pu interpréter les résultats à l'aide des seuils que nous avions définis. Chez les trois espèces, des particularités ont pu être observées, y compris au niveau de produits de gènes similaires. Elles concernent notamment des processus biologiques et des composants cellulaires. Les fonctions moléculaires présentent une forte similarité et peu de particularités. Ces résultats sont biologiquement pertinents.Cross-species comparison of metabolic pathways is an important task in biology. It is a major stake for both human health and agronomy. Currently, knowledge is acquired from some experiments on a relatively low number of species referred to as models''. A better understanding of a species determines whether to validate or not an inference made from these experimental data. It also determines whether or to what extent results obtained on model species can be transposed to another species. This thesis proposes a cross-species metabolic pathways comparison method. Our method compares each step of a metabolic pathway using the associated Gene Ontology annotations. This work validates the interest of the semantic similarity measures for interpreting these annotations, proposes to use jointly a semantic particularity measure and proposes a method based on similarity and particularity patterns to interpret each metabolic pathway step. Several gene products are involved throughout a metabolic pathway. They are associated to some annotations in order to describe their biological roles. Based on a shared ontology, these annotations allow to compare data from different species and to take into account several level of abstraction. Several semantic measures quantifying the similarity between gene products from their annotations have been developed previously. We have identified and used a semantic similarity measure appropriate for cross-species comparisons. Because they focus on the common part of the compared gene products, the semantic similarity measures ignore their specific characteristics. Therefore, cross-species metabolic pathways comparison has to quantify not only the similarity of the gene products involved, but also their particularity. We have developed a semantic particularity measure addressing this issue. For each pathway step, we proposed to create a profile combining its semantic similarity and its two semantic particularity values. Concerning the results interpretation, it is not possible to establish formally that two gene products are similar or that one of them have some significant particularities without having a similarity threshold and a particularity threshold. So far, these interpretations were based on an implicit or an arbitrary threshold. To address this gap, we developed a threshold definition method for the semantic similarity and particularity measures. We last applied a cross-species similarity measure and our particularity measure to compare the lipid metabolism between human, mice and chicken. We then interpreted the results using the previously defined thresholds. In all three species, we observed some particularities, including on similar genes. They concerned notably some biological processes and cellular components. The molecular functions present a strong similarity and few particularities. These results are biologically relevant.RENNES1-Bibl. électronique (352382106) / SudocSudocFranceF

Ontology-based Semantic Harmonization of HIV-associated Common Data Elements for Integration of Diverse HIV Research Datasets

Analysis of integrated, diverse, Human Immunodeficiency Virus (HIV)-associated datasets can increase knowledge and guide the development of novel and effective interventions for disease prevention and treatment by increasing breadth of variables and statistical power, particularly for sub-group analyses. This topic has been identified as a National Institutes of Health research priority, but few efforts have been made to integrate data across HIV studies. Our aims were to: 1) Characterize the semantic heterogeneity (SH) in the HIV research domain; 2) Identify HIV-associated common data elements (CDEs) in empirically generated and knowledge-based resources; 3) Create a formal representation of HIV-associated CDEs in the form of an HIV-associated Entities in Research Ontology (HERO); 4) Assess the feasibility of using HERO to semantically harmonize HIV research data. Our approach was guided by information/knowledge theory and the DIKW (Data Information Knowledge Wisdom) hierarchical model. Our systematized review of the literature revealed that synergistic use of both ontologies and CDEs included integration, interoperability, data exchange, and data standardization. Moreover, methods and tools included use of experts for CDE identification, the Unified Medical Language System, natural language processing, Extensible Markup Language, Health Level 7, and ontology development tools (e.g., Protégé). Additionally, evaluation methods included expert assessment, quantification of mapping tasks between raters, assessment of interrater reliability, and comparison to established standards. We used these findings to inform our process for achieving the study aims. For Aim 1, we analyzed eight disparate HIV-associated data dictionaries and developed a String Metric-assisted Assessment of Semantic Heterogeneity (SMASH) method, which aided identification of 127 (13%) homogeneous data element (DE) pairs and 1,048 (87%) semantically heterogeneous DE pairs. Most heterogeneous pairs (97%) were semantically-equivalent/syntactically-different, allowing us to determine that SH in the HIV research domain was high. To achieve Aim 2, we used Clinicaltrials.gov, Google Search, and text mining in R to identify HIV-associated CDEs in HIV journal articles, HIV-associated datasets, AIDSinfo HIV/AIDS Glossary, AIDSinfo Drug Database, Logical Observation Identifiers Names and Codes (LOINC), Systematized Nomenclature of Medicine (SNOMED), and RxNORM (understood as prescription normalization). Two HIV experts then manually reviewed DEs from the journal articles and data dictionaries to confirm DE commonality and resolved semantic discrepancies through discussion. Ultimately, we identified 2,179 unique CDEs. Of all CDEs, data-driven approaches identified 2,055 (94%) (999 from the HIV/AIDS Glossary, 398 from the Drug Database, 91 from journal articles, and a total of 567 from LOINC, SNOMED, and RxNorm cumulatively). Expert-based approaches identified 124 (6%) unique CDEs from data dictionaries and confirmed the 91 CDEs from journal articles. In Aim 3, we used the Protégé suite of ontology development tools and the 2,179 CDEs to develop the HERO. We modeled the ontology using the semantic structure of the Medical Entities Dictionary, available hierarchical information from the CDE knowledge resources, and expert knowledge. The ontology fulfilled most relevant criteria from Cimino’s desiderata and OntoClean ontology engineering principles, and it successfully answered eight competency questions. Finally, for Aim 4, we assessed the feasibility of using HERO to semantically harmonize and integrate the data dictionaries from two diverse HIV-associated datasets. Two HIV experts involved in the development of HERO independently assessed each data dictionary. Of the 367 DEs in data dictionary 1 (D1), 181 (49.32%) were identified as CDEs and 186 (50.68%) were not CDEs, and of the 72 DEs in data dictionary 2 (D2), 37 (51.39%) were CDEs and 35 (48.61%) were not CDEs. The HIV experts then traversed HERO’s hierarchy to map CDEs from D1 and D2 to CDEs in HERO. Of the 181 CDEs in D1, 156 (86.19%) were found in HERO, and 25 (13.81%) were not. Similarly, of the 37 CDEs in D2 32 (86.48%) were found in HERO, and 5 (13.51%) were not. Interrater reliability for CDE identification as measured by Cohen’s Kappa was 0.900 for D1 and 0.892 for D2. Cohen’s Kappas for CDEs in D1 and D2 that were also identified in HERO were 0.885 and 0.688, respectively. Subsequently, to demonstrate the integration of the two HIV-associated datasets, a sample of semantically harmonized CDEs in both datasets was categorically selected (e.g. administrative, demographic, and behavioral), and D2 sample size increases were calculated for race (e.g., White, African American/Black, Asian/Pacific Islander, Native American/Indian, and Hispanic/Latino) and for “intravenous drug use” from the integrated datasets. The average increase of D2 CDEs for six selected CDEs was 1,928%. Despite the limitation of HERO developers also serving as evaluators, the contributions of the study to the fields of informatics and HIV research were substantial. Confirmatory contributions include: identification of effective CDE/ontology tools, and use of data-driven and expert-based methods. Novel contributions include: development of SMASH and HERO; and new contributions include documenting that SH is high in HIV-associated datasets, identifying 2,179 HIV-associated CDEs, creating two additional classifications of SH, and showing that using HERO for semantic harmonization of HIV-associated data dictionaries is feasible. Our future work will build upon this research by expanding the numbers and types of datasets, refining our methods and tools, and conducting an external evaluation

Decision Support Systems

Decision support systems (DSS) have evolved over the past four decades from theoretical concepts into real world computerized applications. DSS architecture contains three key components: knowledge base, computerized model, and user interface. DSS simulate cognitive decision-making functions of humans based on artificial intelligence methodologies (including expert systems, data mining, machine learning, connectionism, logistical reasoning, etc.) in order to perform decision support functions. The applications of DSS cover many domains, ranging from aviation monitoring, transportation safety, clinical diagnosis, weather forecast, business management to internet search strategy. By combining knowledge bases with inference rules, DSS are able to provide suggestions to end users to improve decisions and outcomes. This book is written as a textbook so that it can be used in formal courses examining decision support systems. It may be used by both undergraduate and graduate students from diverse computer-related fields. It will also be of value to established professionals as a text for self-study or for reference