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Data sharing is critical to advance genomic research by reducing the demand to collect new data by reusing and combining existing data and by promoting reproducible research. The Cancer Genome Atlas (TCGA) is a popular resource for individual-level genotype-phenotype cancer related data. The Database of Genotypes and Phenotypes (dbGaP) contains many datasets similar to those in TCGA. We have created a software pipeline that will allow researchers to discover relevant genomic data from dbGaP, based on matching TCGA metadata. The resulting research provides an easy to use tool to connect these two data sources.
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OBJECTIVES: To contrast the coverage of diseases between the Disease Ontology (DO) and SNOMED CT, and to compare the hierarchical structure of the two ontologies. METHODS: We establish a reference list of mappings. We characterize unmapped concepts in DO semantically and structurally. Finally, we compare the hierarchical structure between the two ontologies. RESULTS: Overall, 4478 (65%) the 6931 DO concepts are mapped to SNOMED CT. The cancer and neoplasm subtrees of DO account for many of the unmapped concepts. The most frequent differentiae in unmapped concepts include morphology (for cancers and neoplasms), specific subtypes (for rare genetic disorders), and anatomical subtypes. Unmapped concepts usually form subtrees, and less often correspond to isolated leaves or intermediary concepts. CONCLUSION: This detailed analysis of the gaps in coverage and structural differences between DO and SNOMED CT contributes to the interoperability between these two ontologies and will guide further validation of the mapping.
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Interoperabilidad de la Información en Salud , Systematized Nomenclature of Medicine , Humanos , Investigación , Terminología como AsuntoRESUMEN
The worldwide incidence of melanoma is rising faster than any other cancer, and prognosis for patients with metastatic disease is poor. Current targeted therapies are limited in their durability and/or effect size in certain patient populations due to acquired mechanisms of resistance. Thus, the development of synergistic combinatorial treatment regimens holds great promise to improve patient outcomes. We have previously shown that a model for in-silico knowledge discovery, Translational Ontology-anchored Knowledge Discovery Engine (TOKEn), is able to generate valid relationships between bimolecular and clinical phenotypes. In this study, we have aggregated observational and canonical knowledge consisting of melanoma-related biomolecular entities and targeted therapeutics in a computationally tractable model. We demonstrate here that the explicit linkage of therapeutic modalities with biomolecular underpinnings of melanoma utilizing the TOKEn pipeline yield a set of informed relationships that have the potential to generate combination therapy strategies.
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Protocolos de Quimioterapia Combinada Antineoplásica/uso terapéutico , Sistemas de Información en Farmacia Clínica/organización & administración , Bases de Datos Farmacéuticas/clasificación , Bases del Conocimiento , Melanoma/tratamiento farmacológico , Neoplasias Cutáneas/tratamiento farmacológico , Protocolos de Quimioterapia Combinada Antineoplásica/clasificación , Minería de Datos/métodos , Sistemas de Apoyo a Decisiones Clínicas/organización & administración , Aprendizaje Automático , Melanoma/clasificación , Procesamiento de Lenguaje Natural , Neoplasias Cutáneas/clasificaciónRESUMEN
Systems designed to expedite data preprocessing tasks such as data discovery, interpretation, and integration that are required before data analysis drastically impact the pace of biomedical informatics research. Current commercial interactive and real-time data integration tools are designed for large-scale business analytics requirements. In this paper we identify the need for end-to-end data fusion platforms from the researcher's perspective, supporting ad-hoc data interpretation and integration.
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Biología Computacional/métodos , Sistemas de Administración de Bases de Datos , Bases de Datos Factuales , Almacenamiento y Recuperación de la Información/métodos , Bases del Conocimiento , Proyectos de Investigación , Sistemas de Computación , Evaluación de NecesidadesRESUMEN
An important factor influencing the pace of research activity is the ability of researchers to discover and leverage heterogeneous resources. Usually, researcher profiles, laboratory equipment, data samples, clinical trials, and other research resources are stored in heterogeneous datasets in large organizations. Emergent semantic web technologies provide novel approaches to discover, annotate and consequently link such resources. In this manuscript, we describe the design of Research Integrative Query (ResearchIQ) tool, a semantically anchored resource discovery platform that facilitates semantic discovery of local and publically available data through a single web portal designed for researchers in the biomedical informatics domain within The Ohio State University.