Browsing by Subject "knowledge integration"

Now showing 1 - 2 of 2
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    A framework for identifying genotypic information from clinical records: exploiting integrated ontology structures to transfer annotations between ICD codes and Gene Ontologies
    (IEEE, 2015-09) Hashemikhabir, Seyedsasan; Xia, Ran; Xiang, Yang; Janga, Sarath Chandra; Department of Biohealth Informatics, School of Informatics and Computing
    Although some methods are proposed for automatic ontology generation, none of them address the issue of integrating large-scale heterogeneous biomedical ontologies. We propose a novel approach for integrating various types of ontologies efficiently and apply it to integrate International Classification of Diseases, Ninth Revision, Clinical Modification (ICD9CM) and Gene Ontologies (GO). This approach is one of the early attempts to quantify the associations among clinical terms (e.g. ICD9 codes) based on their corresponding genomic relationships. We reconstructed a merged tree for a partial set of GO and ICD9 codes and measured the performance of this tree in terms of associations’ relevance by comparing them with two well-known disease-gene datasets (i.e. MalaCards and Disease Ontology). Furthermore, we compared the genomic-based ICD9 associations to temporal relationships between them from electronic health records. Our analysis shows promising associations supported by both comparisons suggesting a high reliability. We also manually analyzed several significant associations and found promising support from literature.
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    Integrated Education of Data Analytics and Information Security through Cross-Curricular Activities
    (IEEE, 2019) Luo, Xiao; Justice, Connie; Sorge, Brandon Herald; Computer Information and Graphics Technology, School of Engineering and Technology
    The National Research Council's report states that cross-sectional studies of multiple courses within a discipline, or all courses in a major, would enhance the understanding of how people learn the concepts, practices, and ways of thinking of science and engineering and the nature and development of expertise in a discipline. In science and engineering, ever-evolving technology and information make integrative abilities necessary and especially valuable. In this study, we investigated cross-curricular pedagogy, by engaging undergraduate students of two disciplines in collaboration on a common, context-connected project, so that students are better prepared for solving interdisciplinary problems in career settings. We implemented cross-curricular pedagogy in a network security course and a big data analytics course. The era of big data enables data-driven malicious detection, and big data analytics techniques have been applied to analyzing network logs to reinforce information security and predict abnormal behaviors, so these domains overlap. We investigated two forms of cross-curricular activities: one was integrated instructional units, and the other was cross-curricular knowledge integration projects. The results show significant improvements in students confidence in solving cross-disciplinary problems and a much better understanding of data analytics and information security, as well as the connections between them. This project is the first to study the loose integration of two context-connected courses that are taught in parallel.