Browsing by Author "Vasilevsky, Nicole"

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    The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species
    (Oxford Journals, 2016-11-26) Mungall, Chris; McMurry, Julie A.; Köhler, Sebastian; Balhoff, James P.; Borromeo, Charles; Brush, Matthew; Carbon, Seth; Conlin, Tom; Dunn, Nathan; Engelstad, Mark; Foster, Erin D.; Gourdine, J.P.; Jacobsen, Julius O.B.; Keith, Dan; Laraway, Bryan; Lewis, Suzanna E.; Xuan, Jeremy N.; Shefchek, Kent; Vasilevsky, Nicole; Yuan, Zhou; Washington, Nicole; Hochheiser, Harry; Groza, Tudor; Smedley, Damian; Robinson, Peter N.; Haendel, Melissa A.
    The correlation of phenotypic outcomes with genetic variation and environmental factors is a core pursuit in biology and biomedicine. Numerous challenges impede our progress: patient phenotypes may not match known diseases, candidate variants may be in genes that have not been characterized, model organisms may not recapitulate human or veterinary diseases, filling evolutionary gaps is difficult, and many resources must be queried to find potentially significant genotype–phenotype associations. Non-human organisms have proven instrumental in revealing biological mechanisms. Advanced informatics tools can identify phenotypically relevant disease models in research and diagnostic contexts. Large-scale integration of model organism and clinical research data can provide a breadth of knowledge not available from individual sources and can provide contextualization of data back to these sources. The Monarch Initiative (monarchinitiative.org) is a collaborative, open science effort that aims to semantically integrate genotype–phenotype data from many species and sources in order to support precision medicine, disease modeling, and mechanistic exploration. Our integrated knowledge graph, analytic tools, and web services enable diverse users to explore relationships between phenotypes and genotypes across species.
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    Semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery
    (Springer Nature, 2019) Zhang, Xingmin Aaron; Yates, Amy; Vasilevsky, Nicole; Gourdine, J. P.; Callahan, Tiffany J.; Carmody, Leigh C.; Danis, Daniel; Joachimiak, Marcin P.; Ravanmehr, Vida; Pfaff, Emily R.; Champion, James; Robasky, Kimberly; Xu, Hao; Fecho, Karamarie; Walton, Nephi A.; Zhu, Richard L.; Ramsdill, Justin; Mungall, Christopher J.; Köhler, Sebastian; Haendel, Melissa A.; McDonald, Clement J.; Vreeman, Daniel J.; Peden, David B.; Bennett, Tellen D.; Feinstein, James A.; Martin, Blake; Stefanski, Adrianne L.; Hunter, Lawrence E.; Chute, Christopher G.; Robinson, Peter N.; Medicine, School of Medicine
    Electronic Health Record (EHR) systems typically define laboratory test results using the Laboratory Observation Identifier Names and Codes (LOINC) and can transmit them using Fast Healthcare Interoperability Resource (FHIR) standards. LOINC has not yet been semantically integrated with computational resources for phenotype analysis. Here, we provide a method for mapping LOINC-encoded laboratory test results transmitted in FHIR standards to Human Phenotype Ontology (HPO) terms. We annotated the medical implications of 2923 commonly used laboratory tests with HPO terms. Using these annotations, our software assesses laboratory test results and converts each result into an HPO term. We validated our approach with EHR data from 15,681 patients with respiratory complaints and identified known biomarkers for asthma. Finally, we provide a freely available SMART on FHIR application that can be used within EHR systems. Our approach allows readily available laboratory tests in EHR to be reused for deep phenotyping and exploits the hierarchical structure of HPO to integrate distinct tests that have comparable medical interpretations for association studies.