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  1. Book ; Online: Knowledge-Driven Mechanistic Enrichment of the Preeclampsia Ignorome

    Callahan, Tiffany J. / Stefanski, Adrianne L. / Kim, Jin-Dong / Baumgartner Jr., William A. / Wyrwa, Jordan M. / Hunter, Lawrence E.

    2022  

    Abstract: Preeclampsia is a leading cause of maternal and fetal morbidity and mortality. Currently, the only definitive treatment of preeclampsia is delivery of the placenta, which is central to the pathogenesis of the disease. Transcriptional profiling of human ... ...

    Abstract Preeclampsia is a leading cause of maternal and fetal morbidity and mortality. Currently, the only definitive treatment of preeclampsia is delivery of the placenta, which is central to the pathogenesis of the disease. Transcriptional profiling of human placenta from pregnancies complicated by preeclampsia has been extensively performed to identify differentially expressed genes (DEGs). DEGs are identified using unbiased assays, however, the decisions to investigate DEGs experimentally are biased by many factors, causing many DEGs to remain uninvestigated. A set of DEGs which are associated with a disease experimentally, but which have no known association with the disease in the literature is known as the ignorome. Preeclampsia has an extensive body of scientific literature, a large pool of DEG data, and only one definitive treatment. Tools facilitating knowledge-based analyses, which are capable of combining disparate data from many sources in order to suggest underlying mechanisms of action, may be a valuable resource to support discovery and improve our understanding of this disease. In this work we demonstrate how a biomedical knowledge graph (KG) can be used to identify novel preeclampsia molecular mechanisms. Existing open source biomedical resources and publicly available high-throughput transcriptional profiling data were used to identify and annotate the function of currently uninvestigated preeclampsia-associated DEGs. Experimentally investigated genes associated with preeclampsia were identified from PubMed abstracts using text-mining methodologies. The relative complement of the text-mined- and meta-analysis-derived lists were identified as the uninvestigated preeclampsia-associated DEGs (n=445), i.e., the preeclampsia ignorome. Using the KG to investigate relevant DEGs revealed 53 novel clinically relevant and biologically actionable mechanistic associations.

    Comment: Preprint of an article submitted for consideration in Pacific Symposium on Biocomputing \copyright 2022 copyright World Scientific ...
    Keywords Quantitative Biology - Genomics ; Computer Science - Artificial Intelligence
    Subject code 610
    Publishing date 2022-07-27
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Book ; Online: An Open-Source Knowledge Graph Ecosystem for the Life Sciences

    Callahan, Tiffany J. / Tripodi, Ignacio J. / Stefanski, Adrianne L. / Cappelletti, Luca / Taneja, Sanya B. / Wyrwa, Jordan M. / Casiraghi, Elena / Matentzoglu, Nicolas A. / Reese, Justin / Silverstein, Jonathan C. / Hoyt, Charles Tapley / Boyce, Richard D. / Malec, Scott A. / Unni, Deepak R. / Joachimiak, Marcin P. / Robinson, Peter N. / Mungall, Christopher J. / Cavalleri, Emanuele / Fontana, Tommaso /
    Valentini, Giorgio / Mesiti, Marco / Gillenwater, Lucas A. / Santangelo, Brook / Vasilevsky, Nicole A. / Hoehndorf, Robert / Bennett, Tellen D. / Ryan, Patrick B. / Hripcsak, George / Kahn, Michael G. / Bada, Michael / Baumgartner Jr, William A. / Hunter, Lawrence E.

    2023  

    Abstract: Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge ... ...

    Abstract Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoints and abstraction algorithms), and benchmarks (e.g., prebuilt KGs and embeddings). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability.
    Keywords Computer Science - Artificial Intelligence ; Computer Science - Computational Engineering ; Finance ; and Science
    Subject code 004
    Publishing date 2023-07-11
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  3. Book ; Online: Ontologizing Health Systems Data at Scale

    Callahan, Tiffany J. / Stefanski, Adrianne L. / Wyrwa, Jordan M. / Zeng, Chenjie / Ostropolets, Anna / Banda, Juan M. / Baumgartner Jr., William A. / Boyce, Richard D. / Casiraghi, Elena / Coleman, Ben D. / Collins, Janine H. / Deakyne-Davies, Sara J. / Feinstein, James A. / Haendel, Melissa A. / Lin, Asiyah Y. / Martin, Blake / Matentzoglu, Nicolas A. / Meeker, Daniella / Reese, Justin /
    Sinclair, Jessica / Taneja, Sanya B. / Trinkley, Katy E. / Vasilevsky, Nicole A. / Williams, Andrew / Zhang, Xingman A. / Denny, Joshua C. / Robinson, Peter N. / Ryan, Patrick / Hripcsak, George / Bennett, Tellen D. / Hunter, Lawrence E. / Kahn, Michael G.

    Making Translational Discovery a Reality

    2022  

    Abstract: Background: Common data models solve many challenges of standardizing electronic health record (EHR) data, but are unable to semantically integrate all the resources needed for deep phenotyping. Open Biological and Biomedical Ontology (OBO) Foundry ... ...

    Abstract Background: Common data models solve many challenges of standardizing electronic health record (EHR) data, but are unable to semantically integrate all the resources needed for deep phenotyping. Open Biological and Biomedical Ontology (OBO) Foundry ontologies provide computable representations of biological knowledge and enable the integration of heterogeneous data. However, mapping EHR data to OBO ontologies requires significant manual curation and domain expertise. Objective: We introduce OMOP2OBO, an algorithm for mapping Observational Medical Outcomes Partnership (OMOP) vocabularies to OBO ontologies. Results: Using OMOP2OBO, we produced mappings for 92,367 conditions, 8611 drug ingredients, and 10,673 measurement results, which covered 68-99% of concepts used in clinical practice when examined across 24 hospitals. When used to phenotype rare disease patients, the mappings helped systematically identify undiagnosed patients who might benefit from genetic testing. Conclusions: By aligning OMOP vocabularies to OBO ontologies our algorithm presents new opportunities to advance EHR-based deep phenotyping.

    Comment: Supplementary Material is included at the end of the manuscript
    Keywords Computer Science - Databases ; Computer Science - Artificial Intelligence ; J.3
    Subject code 006
    Publishing date 2022-09-10
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

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