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In addition to making use of the genome sequence data published by the HGP, HapMap researchers utilized the Database of Single Nucleotide Polymorphisms (dbSNP) maintained by the National Center for Biotechnology Information, a branch of the U.S. National Institutes of Health.
The employees then utilized the database to feedback their observations.
Here, we utilized the database from Taiwan National Health Insurance NHII) to evaluate its correlation under a population-based, case control analysis.
For a more comprehensive overview of functional annotation, we utilized The Database for Annotation, Visualization and Integrated Discovery (DAVID) [ 22, 23] to analyze the enriched clusters of Gene Ontology (GO) biological process and molecular function terms.
Here, depending on the method and using a comprehensive analysis of the protein interaction network, we fully utilized the database containing reported PC-related genes and predicted potential genes involved in PC.
In this study, we utilized the database of the registry of Japanese RA patients on biologics for long-term safety (REAL), a prospective, multi-center cohort with a large number of patients, and herein report IRs for each category of SAEs for TCZ with hazard ratios (HRs) for SAEs and SIs from the use of TCZ compared to the use of TNFIs.
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We utilized the databases of two large prospective cohort studies; the Nurses Health Study NHSHS, N = 121,700 women followed since 1976) [25], [26], and the Health Professionals Follow-up Study HPFSS, N = 51,500 men followed since 1986) [26].
In this study, we utilized the databases and analytical approaches available from the Gene Expression Omnibus and Cancer Genome Anatomy Project to systematically analyze the expression of 22 S100 genes in normal and cancerous stomach tissues.
For this study, we utilized the databases of two large prospective cohort studies; the Nurses Health Studyy (N = 121,700 women followed since 1976) [ 34], and the Health Professional Follow-up Study (N = 51,500 men followed since 1986) [ 35].
In order to recruit patients into this study, we utilized the databases of two large prospective cohort studies: the Nurses Health Studyy (N = 121,700 women) [ 35], and the Health Professional Follow-up Study (N = 51,500 men) [ 36].
Utilizing the database, which consists of process information, the algorithm enables the design of the process without the need for experts who are skilled and experienced in the forming processes.
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