Philosophiae Doctor - PhD / To ameliorate Hepatitis C Virus (HCV) therapeutic and diagnostic challenges requires robust intervention strategies, including approaches that leverage the plethora of rich data published in biomedical literature to gain greater understanding of HCV pathobiological mechanisms. The multitudes of metadata originating from HCV clinical trials as well as low and high-throughput experiments embedded in text corpora can be mined as data sources for the implementation of HCV-specific resources. HCV-customized resources may support the generation of worthy and testable hypothesis and reveal potential research clues to augment the pursuit of efficient diagnostic biomarkers and therapeutic targets. This research thesis report the development of two freely available HCV-specific web-based resources: (i) Dragon Exploratory System on Hepatitis C Virus (DESHCV) accessible via http://apps.sanbi.ac.za/DESHCV/ or http://cbrc.kaust.edu.sa/deshcv/ and (ii) Hepatitis C Virus Protein Interaction Database (HCVpro) accessible via http://apps.sanbi.ac.za/hcvpro/ or http://cbrc.kaust.edu.sa/hcvpro/. DESHCV is a text mining system implemented using named concept recognition and cooccurrence based approaches to computationally analyze about 32, 000 HCV related abstracts obtained from PubMed. As part of DESHCV development, the pre-constructed dictionaries of the Dragon Exploratory System (DES) were enriched with HCV biomedical concepts, including HCV proteins, name variants and symbols to enable HCV knowledge specific exploration. The DESHCV query inputs consist of user-defined keywords, phrases and concepts. DESHCV is therefore an information extraction tool that enables users to computationally generate association between concepts and support the prediction of potential hypothesis with diagnostic and therapeutic relevance. Additionally, users can retrieve a list of abstracts containing tagged concepts that can be used to overcome the herculean task of manual biocuration. DESHCV has been used to simulate previously reported thalidomide-chronic hepatitis C hypothesis and also to model a potentially novel thalidomide-amantadine hypothesis. HCVpro is a relational knowledgebase dedicated to housing experimentally detected HCV-HCV and HCV-human protein interaction information obtained from other databases and curated from biomedical journal articles. Additionally, the database contains consolidated biological information consisting of hepatocellular carcinoma (HCC) related genes, comprehensive reviews on HCV biology and drug development, functional genomics and molecular biology data, and cross-referenced links to canonical pathways and other essential biomedical databases. Users can retrieve enriched information including interaction metadata from HCVpro by using protein identifiers, gene chromosomal locations, experiment types used in detecting the interactions, PubMed IDs of journal articles reporting the interactions, annotated protein interaction IDs from external databases, and via “string searches”. The utility of HCVpro has been demonstrated by harnessing integrated data to suggest putative baseline clues that seem to support current diagnostic exploratory efforts directed towards vimentin. Furthermore, eight genes comprising of ACLY, AZGP1, DDX3X, FGG, H19, SIAH1, SERPING1 and THBS1 have been recommended for possible investigation to evaluate their diagnostic potential. The data archived in HCVpro can be utilized to support protein-protein interaction network-based candidate HCC gene prioritization for possible validation by experimental biologists. / South Africa

Research and experimentation in various scientific fields are based on the knowledge and ideas from scholarly literature. The advancement of research and development has, thus, strengthened the importance of literary analysis and understanding. However, in recent years, researchers have been facing massive scholarly documents published at an exponentially increasing rate. Analyzing this vast number of publications is far beyond the capability of individual researchers. This dissertation is motivated by the need for large scale analyses of the exploding number of scholarly literature for scientific knowledge discovery. In the first part of this dissertation, the interdependencies between scholarly literature are studied. First, I develop Delve – a data-driven search engine supported by our designed semi-supervised edge classification method. This system enables users to search and analyze the relationship between datasets and scholarly literature. Based on the Delve system, I propose to study information extraction as a node classification problem in attributed networks. Specifically, if we can learn the research topics of documents (nodes in a network), we can aggregate documents by topics and retrieve information specific to each topic (e.g., top-k popular datasets). Node classification in attributed networks has several challenges: a limited number of labeled nodes, effective fusion of topological structure and node/edge attributes, and the co-existence of multiple labels for one node. Existing node classification approaches can only address or partially address a few of these challenges. This dissertation addresses these challenges by proposing semi-supervised multi-class/multi-label node classification models to integrate node/edge attributes and topological relationships. The second part of this dissertation examines the problem of analyzing the interdependencies between terms in scholarly literature. I present two algorithms for the automatic hypothesis generation (HG) problem, which refers to the discovery of meaningful implicit connections between scientific terms, including but not limited to diseases, drugs, and genes extracted from databases of biomedical publications. The automatic hypothesis generation problem is modeled as a future connectivity prediction in a dynamic attributed graph. The key is to capture the temporal evolution of node-pair (term-pair) relations. Experiment results and case study analyses highlight the effectiveness of the proposed algorithms compared to the baselines’ extension.

There is much debate on the best way to educate students on how to generate hypotheses to enhance clinical reasoning in nursing education. To increase opportunities for nursing programs to promote the discovery of accurate and broad-level hypotheses, scholars recommend abductive reasoning which offers an alternative approach to hypothetico-deductive reasoning. This study explored the effects of abductive reasoning training on hypothesis generation abilities (accuracy, expertise, breadth) of first and second year baccalaureate nursing students in a problem-based learning curriculum. A quasi-experiment with 64 participants (29 control, 35 experimental) was conducted. Based on their allocation, study participants either took part in abductive reasoning training or informal group discussion. Three different test questionnaires, each with a unique care scenario, were used to assess participants’ hypothesis generation abilities at baseline, immediate post-test and one-week follow-up. Content validity for care scenarios and other study materials was obtained from content academic experts. Compared to control participants, experimental participants showed significant improvements at follow-up on hypothesis accuracy (p=0.05), expertise (p=0.006), and breadth (p=0.003). While control participants’ hypotheses displayed a superficial understanding of care situations, experimental participants’ hypotheses reflected increased accuracy, expertise and breadth. This study shows that abductive reasoning, as a scaffolding teaching and learning strategy, can allow nursing students to discover underlying salient patterns in order to better understand and explain the complex realities of care situations. Educating nursing students in abductive reasoning could enable them to adapt existing competencies when trying to accurately and holistically understand newer complex care situations. This could lead to a more holistic, person-based approach to care which will allow nursing students to see various health-related issues as integrated rather than separate. / Thesis / Doctor of Philosophy (PhD) / This study explored the effects of a training program on hypothesis generation abilities of nursing students. The training program aimed to teach students how to think more broadly about care situations. Student’s hypothesis generation abilities were measured through the use of three care scenarios, each of which was presented before, immediately after and one-week after the training program. Only first and second year nursing students were included in the study. About half of the students were provided with the training while the other half were provided with informal discussion about hypothesis generation. After one-week, it was discovered that students who received the training had improved significantly in their ability to generate broad hypotheses. These students also generated hypotheses that were more accurate than the other group of students who did not receive the training. Due to the training, students’ abilities in discovering the important aspects of the care situation also improved.

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<p>To ameliorate Hepatitis C Virus (HCV) therapeutic and diagnostic challenges requires robust intervention strategies, including approaches that leverage the plethora of rich data published in biomedical literature to gain greater understanding of HCV pathobiological mechanisms. The multitudes of metadata originating from HCV clinical trials as well as low and high-throughput experiments embedded in text corpora can be mined as data sources for the implementation of HCV-specific resources. HCV-customized resources may support the generation of worthy and testable hypothesis and reveal potential research clues to augment the pursuit of efficient diagnostic biomarkers and therapeutic targets. This research thesis report the development of two freely available HCV-specific web-based resources: (i) Dragon Exploratory System on Hepatitis C Virus (DESHCV) accessible via http://apps.sanbi.ac.za/DESHCV/ or http://cbrc.kaust.edu.sa/deshcv/ and (ii) Hepatitis C Virus Protein Interaction Database (HCVpro) accessible via&nbsp / http://apps.sanbi.ac.za/hcvpro/ or http://cbrc.kaust.edu.sa/hcvpro/. DESHCV is a text mining system implemented using named concept recognition and cooccurrence based&nbsp / approaches to computationally analyze about 32, 000 HCV related abstracts obtained from PubMed. As part of DESHCV development, the pre-constructed dictionaries of the&nbsp / Dragon Exploratory System (DES) were enriched with HCV biomedical concepts, including HCV proteins, name variants and symbols to enable HCV knowledge specific&nbsp / exploration. The DESHCV query inputs consist of user-defined keywords, phrases and concepts. DESHCV is therefore an information extraction tool that enables users to&nbsp / computationally generate association between concepts and support the prediction of potential hypothesis with diagnostic and therapeutic relevance. Additionally, users can&nbsp / retrieve a list of abstracts containing tagged concepts that can be used to overcome the herculean task of manual biocuration. DESHCV has been used to simulate previously&nbsp / reported thalidomide-chronic hepatitis C hypothesis and also to model a potentially novel thalidomide-amantadine hypothesis. HCVpro is a relational knowledgebase dedicated to housing experimentally detected HCV-HCV and HCV-human protein interaction information obtained from other databases and curated from biomedical journal articles.&nbsp / Additionally, the database contains consolidated biological information consisting of hepatocellular carcinoma (HCC) related genes, comprehensive reviews on HCV biology and drug development, functional genomics and molecular biology data, and cross-referenced links to canonical pathways and other essential biomedical databases. Users can retrieve enriched information including interaction metadata from HCVpro by using protein identifiers, gene chromosomal locations, experiment types used in detecting the interactions, PubMed IDs of journal articles reporting the interactions, annotated protein interaction IDs from external databases, and via &ldquo / string searches&rdquo / . The utility of HCVpro&nbsp / has been demonstrated by harnessing integrated data to suggest putative baseline clues that seem to support current diagnostic exploratory efforts directed towards vimentin.&nbsp / Furthermore, eight genes comprising of ACLY, AZGP1, DDX3X, FGG, H19, SIAH1, SERPING1 and THBS1 have been recommended for possible investigation to evaluate their&nbsp / diagnostic potential. The data archived in HCVpro can be&nbsp / utilized to support protein-protein interaction network-based candidate HCC gene prioritization for possible validation by experimental biologists.&nbsp / </p>

<p>To ameliorate Hepatitis C Virus (HCV) therapeutic and diagnostic challenges requires robust intervention strategies, including approaches that leverage the plethora of rich data published in biomedical literature to gain greater understanding of HCV pathobiological mechanisms. The multitudes of metadata originating from HCV clinical trials as well as low and high-throughput experiments embedded in text corpora can be mined as data sources for the implementation of HCV-specific resources. HCV-customized resources may support the generation of worthy and testable hypothesis and reveal potential research clues to augment the pursuit of efficient diagnostic biomarkers and therapeutic targets. This research thesis report the development of two freely available HCV-specific web-based resources: (i) Dragon Exploratory System on Hepatitis C Virus (DESHCV) accessible via http://apps.sanbi.ac.za/DESHCV/ or http://cbrc.kaust.edu.sa/deshcv/ and (ii) Hepatitis C Virus Protein Interaction Database (HCVpro) accessible via&nbsp / http://apps.sanbi.ac.za/hcvpro/ or http://cbrc.kaust.edu.sa/hcvpro/. DESHCV is a text mining system implemented using named concept recognition and cooccurrence based&nbsp / approaches to computationally analyze about 32, 000 HCV related abstracts obtained from PubMed. As part of DESHCV development, the pre-constructed dictionaries of the&nbsp / Dragon Exploratory System (DES) were enriched with HCV biomedical concepts, including HCV proteins, name variants and symbols to enable HCV knowledge specific&nbsp / exploration. The DESHCV query inputs consist of user-defined keywords, phrases and concepts. DESHCV is therefore an information extraction tool that enables users to&nbsp / computationally generate association between concepts and support the prediction of potential hypothesis with diagnostic and therapeutic relevance. Additionally, users can&nbsp / retrieve a list of abstracts containing tagged concepts that can be used to overcome the herculean task of manual biocuration. DESHCV has been used to simulate previously&nbsp / reported thalidomide-chronic hepatitis C hypothesis and also to model a potentially novel thalidomide-amantadine hypothesis. HCVpro is a relational knowledgebase dedicated to housing experimentally detected HCV-HCV and HCV-human protein interaction information obtained from other databases and curated from biomedical journal articles.&nbsp / Additionally, the database contains consolidated biological information consisting of hepatocellular carcinoma (HCC) related genes, comprehensive reviews on HCV biology and drug development, functional genomics and molecular biology data, and cross-referenced links to canonical pathways and other essential biomedical databases. Users can retrieve enriched information including interaction metadata from HCVpro by using protein identifiers, gene chromosomal locations, experiment types used in detecting the interactions, PubMed IDs of journal articles reporting the interactions, annotated protein interaction IDs from external databases, and via &ldquo / string searches&rdquo / . The utility of HCVpro&nbsp / has been demonstrated by harnessing integrated data to suggest putative baseline clues that seem to support current diagnostic exploratory efforts directed towards vimentin.&nbsp / Furthermore, eight genes comprising of ACLY, AZGP1, DDX3X, FGG, H19, SIAH1, SERPING1 and THBS1 have been recommended for possible investigation to evaluate their&nbsp / diagnostic potential. The data archived in HCVpro can be&nbsp / utilized to support protein-protein interaction network-based candidate HCC gene prioritization for possible validation by experimental biologists.&nbsp / </p>

Philosophiae Doctor - PhD / To ameliorate Hepatitis C Virus (HCV) therapeutic and diagnostic challenges requires robust intervention strategies, including approaches that leverage the plethora of rich data published in biomedical literature to gain greater understanding of HCV pathobiological mechanisms. The multitudes of metadata originating from HCV clinical trials as well as low and high-throughput experiments embedded in text corpora can be mined as data sources for the implementation of HCV-specific resources. HCV-customized resources may support the generation of worthy and testable hypothesis and reveal potential research clues to augment the pursuit of efficient diagnostic biomarkers and therapeutic targets. This research thesis report the development of two freely available HCV-specific web-based resources: (i) Dragon Exploratory System on Hepatitis C Virus (DESHCV) accessible via http://apps.sanbi.ac.za/DESHCV/ or http://cbrc.kaust.edu.sa/deshcv/ and(ii) Hepatitis C Virus Protein Interaction Database (HCVpro) accessible via http://apps.sanbi.ac.za/hcvpro/ or http://cbrc.kaust.edu.sa/hcvpro/.DESHCV is a text mining system implemented using named concept recognition and cooccurrence based approaches to computationally analyze about 32, 000 HCV related abstracts obtained from PubMed. As part of DESHCV development, the pre-constructed dictionaries of the Dragon Exploratory System (DES) were enriched with HCV biomedical concepts, including HCV proteins, name variants and symbols to enable HCV knowledge specific exploration. The DESHCV query inputs consist of user-defined keywords, phrases and concepts. DESHCV is therefore an information extraction tool that enables users to computationally generate association between concepts and support the prediction of potential hypothesis with diagnostic and therapeutic relevance.Additionally, users can retrieve a list of abstracts containing tagged concepts that can be used to overcome the herculean task of manual biocuration. DESHCV has been used to simulate previously reported thalidomide-chronic hepatitis C hypothesis and also to model a potentially novel thalidomide-amantadine hypothesis.HCVpro is a relational knowledgebase dedicated to housing experimentally detected HCV-HCV and HCV-human protein interaction information obtained from other databases and curated from biomedical journal articles. Additionally, the database contains consolidated biological information consisting of hepatocellular carcinoma(HCC) related genes, comprehensive reviews on HCV biology and drug development,functional genomics and molecular biology data, and cross-referenced links to canonical pathways and other essential biomedical databases. Users can retrieve enriched information including interaction metadata from HCVpro by using protein identifiers,gene chromosomal locations, experiment types used in detecting the interactions, PubMed IDs of journal articles reporting the interactions, annotated protein interaction IDs from external databases, and via “string searches”. The utility of HCVpro has been demonstrated by harnessing integrated data to suggest putative baseline clues that seem to support current diagnostic exploratory efforts directed towards vimentin. Furthermore,eight genes comprising of ACLY, AZGP1, DDX3X, FGG, H19, SIAH1, SERPING1 and THBS1 have been recommended for possible investigation to evaluate their diagnostic potential. The data archived in HCVpro can be utilized to support protein-protein interaction network-based candidate HCC gene prioritization for possible validation by experimental biologists.

L'enseignement du raisonnement clinique infirmier (RCI) est une préoccupation importante des formateurs en sciences infirmières depuis plusieurs années. Les étudiantes en sciences infirmières éprouvent des difficultés à formuler des hypothèses cliniques, à savoir trouver les explications pouvant justifier la coexistence d'une combinaison de données cliniques. Pourtant, la formulation d’hypothèses constitue une étape déterminante du RCI. Dans cette étude qualitative exploratoire, nous avons mis à l'essai une activité d'apprentissage par vignette clinique courte (AVCC) qui fournit aux étudiantes l'occasion d'exercer spécifiquement la formulation d'hypothèses cliniques. L'étude visait à documenter la capacité d'étudiantes de troisième année au baccalauréat en sciences infirmières à formuler des hypothèses cliniques durant l'activité. Dix-sept étudiantes ont été recrutées par convenance et divisées en groupes selon leurs disponibilités. Au total, quatre séances ont eu lieu. Les participantes étaient invitées à réfléchir à une vignette clinique courte et à construire un algorithme qui incluait: 1) leurs hypothèses concernant la nature du problème clinique, 2) les éléments d'informations essentiels à rechercher pour vérifier chaque hypothèse et 3) les moyens pour trouver ces informations. L'observation participante, l'enregistrement audio-vidéo et un questionnaire auto-administré ont servi à collecter les données. Les stratégies de RCI décrites par Fonteyn (1998) ont servi de cadre théorique pour guider l’analyse, sous forme de matrices comprenant des verbatims et des notes de terrain. Les résultats suggèrent que l'AVCC stimule la formulation d'hypothèses cliniques et la réactivation des connaissances antérieures. Cette activité pourrait donc être utile en complément d'autres activités éducatives pour favoriser le développement du RCI chez les étudiantes en sciences infirmières. / Teaching and learning clinical reasoning has been a major concern amongst nurse educators for many years. Hypothesis generation is a critical milestone in clinical nursing reasoning which students are still struggling with at the end of their program. In a qualitative exploratory study, we tested a vignette-based activity to provide to the students an opportunity to specifically practice hypotheses generation. The study aimed at documenting nursing student’s capacity to formulate hypotheses during the activity. Seventeen nursing students in the last semester of their program were recruited by convenience and grouped accordingly to their availability to participate. The activity was held four times. Participants were asked to focus on a brief clinical vignette and to build an algorithm that would include 1) their hypotheses regarding the nature of the problem, 2) the essential pieces of information to collect in order to verify each hypothesis, and 3) the way the information was to be found. The combined methods used for data collection were participative observation, videotaping the activity and a written questionnaire immediately after the activity. Data were then classified in matrices in the form of verbatim and notes using clinical nursing reasoning skills described by Fonteyn (1998) as the theoretical framework. Results suggest that the vignette-based activity does stimulate students to formulate hypotheses. It also stimulates sharing and recollection of knowledge amongst students. This type of activity could therefore be useful in promoting the development of clinical reasoning as a complement to other educative activities used in nursing education programs.

De plus en plus d’organisations scientifiques contemporaines intègrent dans leur processus des foules de participants assignés à des tâches variées, souvent appelés projets de science citoyenne. Ces foules sont une opportunité dans un contexte lié à une avalanche de données massives qui met les structures scientifiques face à leurs limites en terme de ressources et en capacités. Mais ces nouvelles formes de coopération sont déstabilisées par leur nature même dès lors que les tâches déléguées à la foule demandent une certaine inventivité - résoudre des problèmes, formuler des hypothèses scientifiques - et que ces projets sont amenés à être répétés dans l’organisation. A partir de deux études expérimentales basées sur une modélisation originale, cette thèse étudie les mécanismes gestionnaires à mettre en place pour assurer la performance des projets délégués à la foule. Nous montrons que la performance est liée à la gestion de deux types de capitalisation : une capitalisation croisée (chaque participant peut réutiliser les travaux des autres participants) ; une capitalisation séquentielle (capitalisation par les participants puis par les organisateurs). Par ailleurs cette recherche met en avant la figure d’une nouvelle figure managériale pour supporter la capitalisation, le « gestionnaire des foules inventives », indispensable pour le succès des projets. / A growing number of contemporary scientific organizations collaborate with crowds for diverse tasks of the scientific process. These collaborations are often designed as citizen science projects. The collaboration is an opportunity for scientific structures in a context of massive data deluge which lead organizations to face limits in terms of resources and capabilities. However, in such new forms of cooperation a major crisis is caused when tasks delegated to the crowd require a certain inventiveness - solving problems, formulating scientific hypotheses - and when these projects have to be repeated in the organization. From two experimental studies based on an original modeling, this thesis studies the management mechanisms needed to ensure the performance of projects delegated to the crowd. We show that the performance is linked to the management of two types of capitalization: a cross-capitalization (each participant can reuse the work of the other participants); a sequential capitalization (capitalization by the participants then by the organizers). In addition, this research highlights the figure of a new managerial figure to support the capitalization, the "manager of inventive crowds", essential for the success of the projects.

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